The Complete Guide for Machine Learning Beginners

Machine Learning is a vast subject that can often be confusing. This is why we’ve designed a Machine Learning Cheat Sheet to help you as the defacto guide. In this ML cheat sheet, you can find a helpful overview of the most popular machine learning models, along with information on their benefits and drawbacks.

The goal of predictive analytics is to make future predictions using previously obtained data. There are two stages to it:

  • Develop a model from training examples during the training phase.
  • The phase of prediction: Apply the model to forecast an upcoming or unknown result.

Machine Learning

A few helpful process maps and tables of machine learning algorithms are available. Only the most complete ones were selected for inclusion.

1. Supervised Learning

Models used in supervised learning seek to generalize patterns discovered in previously seen data on unseen data by mapping inputs to outputs. Regression models—where we attempt to forecast a continuous variable, such as stock prices—or classification models—where we attempt to predict a binary or multi-class factor, such as whether or not a customer would churn—are two examples of supervised learning models. We’ll go through two well-liked supervised learning model categories in the section below: linear models and tree-based models.

  •  A Linear Model

In order to anticipate unknown data, linear models provide a best-fit line. According to linear models, outputs are just a linear combination of characteristics. We will outline the most popular linear regression models in machine learning, along with their benefits and drawbacks, in this section.

Regular Regression

A straightforward formula for simulating the linear relationship between an input and a continuous target output variable

  • Applications
  1. Stock Price Forecast
  2. Estimating housing price trends
  3. Customer lifetime value forecasting
  • Advantages
  1. Explicit procedure
  2. Results that can be understood by the output coefficient
  3. More quickly trained than alternative machine learning models
  • Disadvantages
  1. Assumes that inputs and outputs are linear
  2. Observant of anomalies
  3. Can underfit with low-dimensional, small-scale data.
  • Tree-based Models

To put it simply, tree-based models extrapolate predictions from decision trees using a set of “if-then” rules. We will outline the most popular linear models in machine learning, along with their benefits and drawbacks, in this section.

Decision Tree

Decision Tree models provide predictions by applying decision rules to the features. It can be applied to regression or classification.

  • Applications
  1. Forecast for customer churn
  2. Modeling of credit scores
  3. Disease prognosis
  • Advantages
  1. Explicit and comprehensible
  2. Accepts missing values
  • Disadvantages
  1. Tendency to overfit
  2. Observant of anomalies

2. Unsupervised Learning

The goal of unsupervised learning is to identify broad trends in data. The most well-known illustration is the clustering or segmentation of users and customers. This kind of segmentation is generally applicable and has a wide range of applications, including for papers, businesses, and genomes. Clustering methods, which learn to group related data points together, and association algorithms, which combine various data points according to pre-established rules, are examples of unsupervised learning.

  • Clustering Models


The most popular clustering method is K-Means, which establishes K groupings based on euclidean distances.

  • Applications
  1. Segmenting customers
  2. Systems of recommendations
  • Advantages
  1. Supports big datasets
  2. Simple to use and understand
  3. Produces compact clusters
  • Disadvantages
  1. Demands the anticipated number of clusters from the start
  2. Possesses issues with a range of cluster sizes and intensities
  • Association

A rule-based method that uses prior knowledge of the characteristics of frequent item sets to identify the most itemsets in a given dataset

  • Applications
  1. Product insertions
  2. Engine recommendations
  3. Optimization of advertising
  • Advantages
  1. Results are perceptible and comprehensible
  2. Exhaustive technique since it uncovers all laws based on support and confidence
  • Disadvantages
  1. Creates a lot of dull item sets
  2. Memory and computation-intensive.
  3. This leads to a lot of overlapping item sets

How to Learn Machine Learning?

Beginners with no programming or mathematical background can learn machine learning. The mathematical, statistical and programming skills can be learn with dedicated learning.

To learn machine learning, you need to:

    1. Focus on short term achievable learning. You don’t need to be a data scientist at Google overnight. Within a year you can definitely have a good understanding of the essentials of machine learning.
    1. Overcome mental barriers. People love to promote that machine learning is an incredibly complex world meant only for the geniuses of the world. Truth is, you can leverage some machine learning very early in your learning process.
    2. Choose a programming language. Python, R, Java, C… The list of programming languages that you can use for Machine Learning is very large. Python is convenient with a large community and lot of packages available. R is arguably better for mathematical modules and applications. C is better for performance, but has an incredible steep learning curve.
    3. Find a course or a program. Some prefer regular academic programs in Universities. I prefer online courses, preferably within a platform that has complete programs that will cover the programming and statistical requirement to improve your machine learning skills over time. DataCamp, Coursera, Udemy, EdX, or even YouTube can be an ally.
  1. Follow a learning schedule that you can keep for at least a year. Learning machine learning takes time. Find a moment every day, every week to learn in a structured way. Keep note of your learnings.
  2. Stop and try ideas for yourself. Working on your own projects early on will help you to keep motivated and will provide early return on investment. Even if it is not the best machine learning at first, it still progresses you can be proud of.

Machine Learning Training Data Sources

Machine Learning works by recognizing the patterns in past data, and then using them to predict future outcomes. To build a successful predictive model, you need data that is relevant to the outcome of interest. This data can take many forms – from number values (temperature, cost of a commodity, etc) to time values (dates, elapsed times) to text, images, video and audio. Fortunately the explosion in computing and sensor technology combined with the internet has enabled us to capture and store data at exponentially increasing rates. The trick is getting the right data for any particular problem – most businesses capture this in their existing technology stacks, and a lot of this data is available for free online.

Structured vs Unstructured Data

Structured versus unstructured data is a common topic in the field of data science, where a structured dataset typically has a well-defined schema and is organized in a table with rows and columns. Unstructured data, on the other hand, is often messy and difficult to process.

Structured and unstructured data can both be the fuel for successful machine learning models.

Let’s dive into the details of structured versus unstructured data, including data formats, data storage, data sources, analysis, and more.

Structured vs Unstructured Data Formats

Structured data is quantifiable and easy to search and analyze, and comes in predefined formats such as CSV, Excel, XML, or JSON, while unstructured data can be in a variety of less well-defined formats including PDFs, images, audio, or video.

Structured data is typically a result of a well-defined schema, which is often created by human experts. It’s easy for people to add or change the schema of structured data, but it can be very difficult to do so with unstructured data.

In short, structured data is searchable and organized in a table, making it easy to find patterns and relationships. It’s also possible to analyze and gain value from unstructured data, such as by using text extraction on PDFs, followed by text classification, but it’s a much more difficult task.

Structured Data Sources

Many popular business tools, like Hubspot, Salesforce, or Snowflake, are sources of structured data.

Akkio’s sample datasets, which are in CSV format, are also examples of structured data. More broadly speaking, any well-defined CSV or Excel file is an example of structured data, millions of examples of which are available on sites like Kaggle or

Unstructured Data Sources

For the purpose of predictive modeling, the most common type of unstructured data is text. This includes text forms, like customer feedback forms, as well as emails, comments on social media sites, product reviews, or even notes taken during sales calls or business meetings.

As we’ve highlighted, unstructured data goes beyond text, and includes audio and video. For example, YouTube reviews are another source of unstructured data. YouTube videos also include AI-generated transcriptions or speech-to-text. Given that text data, text classification could be used to mine those reviews for insights.

Structured vs Unstructured Data Storage

Structured data is often stored in data warehouses while unstructured data is stored in data lakes. A warehouse stores structured datasets and typically relies on more traditional databases like SQL Server and Oracle for storage, while a data lake stores less well-defined datasets.

Structured Data in Real-World AI

Some of the most well-known machine learning models in use today are fueled by structured data.

For example, Amazon uses its database of customer purchasing patterns and preferences to recommend items that are likely to be of interest to a particular customer.

Unstructured Data in Real-World AI

Other machine learning models are fueled by unstructured data.

Tesla uses its fleet of self-driving cars to collect data about driving patterns and conditions. The data is used for teaching self-driving cars how to avoid collisions and navigate through varying driving conditions.

Another example is seen in Google Photos. When you take a photo, Google’s machine learning models scan the image, an unstructured data type, to find what category it falls into. Then, users can search their own, previously unlabeled photos by categories like “Nature” or “People.”

Structured Data Analysis

Most analytics tools are designed for structured data, making it easier than ever to analyze and gain value from structured data.

With Akkio, for instance, you can upload structured data to build and deploy AI models in minutes. In the background, machine learning algorithms scan and digest the tabular data to find patterns, creating a model that can be deployed to find those patterns in new data.

Unstructured Data Analysis

Unstructured data analysis is a less-common task, but it’s still highly important for businesses looking to gain value from their PDFs, image and audio data, and so on.

Analyzing unstructured data is a complicated task, which is why it’s ignored by many businesses.

Unstructured data can be difficult to process and understand because it’s messy and in a variety of formats. Unstructured data may also be qualitative instead of quantitative, making it even harder to analyze.

One use-case for unstructured data is to analyze reviews and comments on social media, both from your own company and from competitors, to inform competitive strategy.

Another use-case is market analysis to find new opportunities. By analyzing unstructured market data, such as social media posts that mention customer needs, businesses can uncover opportunities for new products and features that may meet the needs of these potential customers.

Quantitative vs Qualitative/Categorical Data

Quantitative data is a numerical set of information, such as the height and weight of each person in a group, alongside the size of the group. Quantitative data can be further divided into two sub-categories: Discrete and continuous data.

Discrete data does not include measurements, which are along a spectrum, but instead refers to counting numbers, like the number of products in a customer’s shopping cart, or a count of financial transactions. Continuous data, on the other hand, refers to data that can meaningfully be broken down into smaller units, or placed on a scale, like a customer’s income, an employee’s salary, or the dollar size of a financial transaction.

Qualitative data is non-numeric, such as whether or not a transaction is fraudulent, whether a review has positive or negative sentiment, or whether a sales deal has a high or low likelihood of being closed. Qualitative data is largely categorical, but it also includes things like text, whether it’s a tweet, a customer support ticket, or documentation. By the very meaning of the word, categorical data is simply data relating to categories, while quantitative data relates to quantities.

Let’s dive more deeply into the differences between quantitative and qualitative data, with the latter focusing on categorical data.

How to know whether your data is quantitative or categorical

It can be difficult to determine if your data is categorical or quantitative, but there are a few steps you can take to find out.

If your data has a numerical range of values, like income, age, transaction size, or similar, it’s quantitative. If, on the other hand, there are categories, like “Yes,” “Maybe,” and “No,” it’s categorical.

You should also consider the type of answers you’re expecting from your data. Are you expecting an answer that has a range of values, or just one set of values? If you’re expecting one set of values, like “Fraud” or “Not Fraud,” then it’s categorical. If you’re expecting a range of values, like a certain dollar amount, then it’s quantitative.

Examples of AI models you can make with quantitative data

Quantitative data can be used to fuel a wide range of AI models. Let’s explore a few examples.

  • Forecasting site traffic, given historical traffic data (e.g. if you’re going to run Google Ads on a Saturday night, what are your expected traffic numbers?)
  • Determining the number of customers that will buy something, given historical transaction frequencies (e.g. if you are running a promotion, how many people will purchase an item?)
  • Predicting how much money you will make, given historical revenue (e.g. how many people will click on an ad, and then make a purchase?)
  • Determining what your inventory levels should be, given historical sales figures (e.g. what should your inventory levels look like given your sales figures?)

Quantitative machine learning algorithms can use various forms of regression analysis, for instance, to find the relationship between variables.

To give a simple example, if one variable is the weight of a patient and the other variable is the height of a patient, then the relationship between these variables can be found by running regression analysis on a set of patients.

Examples of AI models you can make with categorical data

Categorical data can also fuel a wide range of AI use-cases. Here are just a couple of examples.

  • Classifying customers into different categories based on the groups of behavior they fall into (e.g. what type of device do they use to browse your website? Do they buy clothes or shoes?)
  • Classifying your ads into different categories based on their effectiveness (e.g. does this ad attract more clicks than another ad?)

Categorical machine learning algorithms including clustering algorithms are used to identify groups within a dataset, where the groups are based on similarity. The technical algorithm names include Naïve Bayes and K-nearest neighbors.

Understanding the intricacies of these complex algorithms used to be a prerequisite to AI modeling, but you can now build and deploy these models in minutes, with no technical expertise needed.

What’s better: Quantitative or categorical data?

There are pros and cons to each type of data, and which data type to use depends on the situation.

Quantitative data is inherently more precise than categorical data, as there’s greater granularity in quantitative data. For example, a height of “72.5 inches” is a lot more precise than the category “tall.” An income of “$12,000” is a lot more precise than the category “poor.”

By using categories, some information can be lost.

For instance, one American with an annual income of $0 and another with an annual income of $12,000 are both classified in the same legal category—poverty—even with significant differences in living situations. Similarly, someone with a net worth of $30 million and someone with a net worth of $100 billion are both classified as Ultra-High-Net-Worth Individuals, even while there are tens of thousands of individuals in the former category and just a few in the latter category.

One disadvantage of quantitative data is that it’s harder to make sense of and model than categorical data. Categorical data inherently simplifies data by reducing the number of data points.

What’s more common: Quantitative or categorical data?

There’s no simple answer as to what’s the more common data type.

Categorical data is often easier to collect. For example, given someone’s Facebook profile, you can likely get data on their race, gender, their favorite food, their interests, their education, their political party, and more, which are all examples of categorical data.

On the other hand, you probably wouldn’t be able to find out their exact income, their weight, their spending habits, or other exact quantitative metrics (with some exceptions like age).

Under the hood, however, the situation is quite different, as Facebook collects vast amounts of data on each of its users, much of which is quantitative, such as the amount of time spent viewing a post, the number of posts viewed, the number of profile views, the number of link clicks, the number of application opens, and so on.

Ultimately, we create large amounts of both data types every day, with virtually every action we take. When you pick up a new smartphone, sensors recognize that it was picked up, by tracking the exact spatial location of your phone at any point in time, which is an example of quantitative data. Then, as it recognizes that your phone was picked up, it may change a variable like “Status” to be “Active” instead of “Inactive,” causing your phone’s lock screen to light up.

Time Series

Time series data is a type of data that records events happening over time, which is especially useful in predicting future events.

To give a very simple example, here’s a time series dataset with three data points: In 1975, Earth’s global surface temperature was anomalous by 0.0 degrees Celsius, in 1995 it was +0.5 degrees Celsius higher than normal, and in 2015 it was +0.9 degrees Celsius higher than normal.

One of the key tenets of time series data is that when something happens is as important as what happens. In marketing, for example, the time it takes a customer to go through the steps of the marketing funnel is an important predictor of revenue.

Common Applications

One of the most important uses of time series data is forecasting. This is because the past is the best predictor of the future. Let’s explore some common applications of time-series data, including forecasting and more.

Marketing Journey

Marketing is a journey, and the customer’s journey through the marketing funnel can seem unpredictable.

However, there are many ways to predict the customer’s journey and reach them at the appropriate time to increase customer engagement and conversion rates. By understanding customer journeys, marketers can also create a more relevant and compelling content experience for each stage of the journey.

For example, if you are running a marketing campaign on Instagram and want to know how many clicks your advertisements will receive, you could forecast clicks based on historical data.

To give another example, time series forecasting can be used to predict when customers will make their next purchase. This allows companies to make decisions about when to launch new products and when to send emails or other consumer messaging.

Revenue Run-Rate

Revenue run-rate is predicting revenue based on what has happened in the past.

This is an important metric for companies because it helps them plan for future revenue needs. Revenue run-rate is an annual metric, which is traditionally calculated by multiplying the average revenue per month by 12, or the average revenue per quarter by 4. This will give a rough estimate of how much revenue the company will have per year.

That said, this is a very rough method of estimating revenue, which can be highly inaccurate. For example, businesses like fitness centers typically out-perform in January, due to New Year’s resolutioners, so they wouldn’t be able to accurately forecast revenue with traditional means. The opposite situation holds true for a landscaping company, which likely won’t see much business in January.

A number of other variables impact revenue as well, from dynamic budgets to new competitors or new product innovation. Traditional calculations, which are based purely on multiplying historical revenue, are ignoring all these other factors.

Using Akkio’s forecasting, you can accurately predict revenue run-rate based on any number of complex variables in your data.

Stock or Crypto Values

Predicting stock and crypto prices is notoriously difficult, especially considering the technical difficulties of manually building and deploying forecasting models.

That said, for investors who are interested in forecasting assets, time series data and machine learning are must-haves. With Akkio, you can connect time series data of stock and crypto assets to forecast prices.

It’s important to remember that stocks and crypto are different types of investments, as crypto markets are much smaller and more volatile. Investors should be wary of their own emotions when investing in stocks and crypto.

Device Health

Manufacturers are using time series AI for predictive maintenance and monitoring equipment health. The AI systems are able to identify when changes need to be made to improve efficiency. They are also able to predict when equipment will break down and send alerts before it happens.

These technologies are saving manufacturers money by not having to spend on unexpected repairs or urgently replace machinery when it is no longer working.

Time Series Datasets

For non-experts, finding high-quality time series datasets is a challenge. Fortunately, there are a huge amount of free, high-quality time series dataset sources available online.

Let’s explore a couple of time series dataset sources.

UCI Time-Series Repository

The UCI repository features 48 time-series datasets, ranging from air quality to sales forecasting data.

Most of the data is offered in CSV format, so it’s easy to read with tools like Akkio, with no manual pre-processing needed. Just connect a dataset, and you’re good to go!

World Bank’s World Development Indicators

The World Bank provides a wildly extensive databank featuring 79 databases for 264 countries with data as far back as 1960.

The World Development Indicators database, for example, includes over 1,440 data columns to pick from, ranging from high-level indicators like “percent access to electricity” to very niche indicators like “rural population living in areas where elevation is below 5 meters.” The Education Statistics database includes almost 4,000 data columns.

There’s no easy answer to how many time-series datasets are offered, but if you treat each potential time-series dataset as a univariate problem, then there are millions of datasets from this source alone (79 databases across 264 countries with an average of 2,000 data columns).

Special considerations for time series data

Time series data can be a particularly tricky data type to work with, for a number of reasons. We’ve highlighted some special considerations to keep in mind when working with time-series data.

  1. Time series data is sequential, but many algorithms for predicting the future are not.

In a time-series dataset, the temporal aspect is crucial, but many machine learning algorithms don’t use this temporal aspect, which creates misleading models that aren’t actually predictive of the future.

For example, a “random walk” model is a stochastic process, which means that it’s simply not possible for it to accurately predict future outcomes from historical data.

To give another example, basic regression models ignore temporal correlation in the observed data and predict the next value of the time series based merely on linear regression methods.

Moreover, many time series models can easily “overfit” to the data, by finding spurious correlations, instead of causal variables.

For example, there’s a positive correlation between ice cream sales and murder, but obviously not because eating ice cream makes you want to murder people. This is what’s known as a “spurious correlation.”

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In the case of ice cream sales and murder, what’s happening is that ice cream sales increase in the summer, which is when more people go outside, causing a natural increase in crime (fewer crimes are committed when everyone’s bundled up inside during the winter, versus, say, when there’s a sports events in the summer with 50,000 attendees packed in a stadium).

  1. It is a lot of work to produce a model that predicts the future from time-series data.

Modeling time series data is an intensive effort, requiring pre-processing, data cleaning, stationarity tests, stationarization methods like detrending or differencing, finding optimal parameters, and more.

Doing this manually requires a high degree of technical expertise, not to mention a large time commitment. With Akkio, these complex processes are automated in the back-end, so you can forecast data effortlessly.

  1. Time series data is often not as accurate when it comes to predicting the future, because many things that have happened in the past are simply no longer relevant to the future.

If you’ve ever considered investing, you’ve likely read a financial disclaimer along the lines of: “Past performance is no guarantee of future results.”

It’s actually a legal requirement for asset management firms to give such a disclaimer, because, well, there’s really no way to know what the future holds. The best we can do is assign probabilities to certain values.

Indeed, even generating accurate probabilities is immensely challenging, as the world is constantly changing. Predicting COVID-19 cases is a great example of the challenges of time series forecasting, as virtually all forecasts failed.

Even now, accurate forecasts are extremely difficult, considering that much past data is no longer relevant for the future, given new vaccines, new strains, and ever-changing regulations around travel, social distancing, quarantines, and so on.

Feature engineering for time series data

Feature engineering is the process of creating new features from existing data.

One challenge with time series data is that it’s often not stationary. Stationarity means that a time series is a sequence of observations of the same variable, taken at equally spaced times. If the observations are equally spaced in time and do not contain any trends or seasonality, then it’s stationary.

Creating stationary data is a form of feature engineering, and the two most common techniques for transforming time series into stationary data are differencing and transforming.

That said, with no-code AI tools like Akkio, you can build and deploy time series models without any manual feature engineering needed, as this is all done automatically after a dataset is connected.

How much data do I need to train an ML model?

Data is the fuel that makes machine learning tick. For the most part, the more data you have, the more accurate your model will be, but there are many cases where you can get by with less.

Machine learning models are pattern matching machines. They can only capture and predict patterns that have been seen before. This is the one big catch with machine learning. If you want to predict what happens with new data, the model has to have seen similar data before.

It’s also important to note that there’s no golden rule for how much data you need. For example, while Akkio’s lead scoring demo dataset has over 40,000 rows of data, the text classification demo dataset has just 1,000 rows of data, and both achieve roughly 90% accuracy. Meanwhile, the credit card fraud demo dataset has nearly 300,000 rows of data!

It’s best to explore the modeling process for your dataset and see what it takes to get high accuracy.

Do you have too little data?

Accurate machine learning models can be made with as little as a few hundred rows of data. If you truly have extremely little data, say less than a few hundred rows, you can try a few things.

One is data augmentation: A process where data is generated by adding in fake data examples. You can also merge in other datasets, whether internal or external, on shared columns to increase the overall dataset size.

For example, suppose you’re building a model to classify customer support tickets based on urgency. If you need more data, you’ll want to ensure that you have a pipeline in place that’s generating this data for you. In such a case, your support teams should be tagging the urgency of incoming tickets, so you can later export this data to fuel your machine learning model.

Depending on the use-case, you can even turn to crowdsourcing platforms like Amazon Mechanical Turk. These platforms allow you to hire people from all over the world to do small tasks for you at low prices, like collecting and labelling data. You may not want to do this if you’re a small company with limited resources, but if you’re a large company and want more data quickly, this may be a good option for you.

Yet another method is to scrape data from the Internet, which is again use-case dependent, but potentially an easy way to boost your dataset size, given the open nature of a lot of Internet data, such as social media posts.

Do you have too much data?

There are instances when it feels like you could have too much data. If your dataset is too large, it becomes difficult to explore and understand what the data is telling you. This is particularly the case with big data in the order of many gigabytes, or even terabytes, which cannot be analyzed with regular tools like Excel or even typical Python Pandas code.

Given that it’s possible to make high-quality machine learning models with much smaller datasets, this problem can be solved by sampling from the larger dataset, and using the derived, smaller sample to build and deploy models.

ML models at any size

A good example of a massive AI model is Google’s latest language model, which is an incredible 1.6 trillion parameters in size—too large for us to practically comprehend, though for comparison, there are just 86 billion neurons in the human brain.

At the same time, it’s possible to build machine learning models that are around 10 orders of magnitude smaller than Google’s language model.

For example, the perceptron is a classifier that was developed in the 1950s. These single-layer neural networks are trained by assigning inputs to different outputs, with the network adjusting its weights until it can correctly predict the output for new inputs. The perceptron is limited by its lack of memory and by not being able to extrapolate relationships between data points that it might not have seen, but at its core, it can be the basis of a functioning model with just a few parameters.

Quantity isn’t everything

It’s important to remember that quantity isn’t everything when it comes to data. Even if you have a lot of data, your model may not work well. In order to have high quality models, you need high quality data. This means that your data needs to be clean and easy to work with so that it can be used effectively.

In other words, it’s better to have a small, high-quality dataset that’s indicative of the problem that you’re trying to solve, than a large, generic dataset riddled with quality issues.

After all, not all data is valuable. As Nate Silver, Founder of FiveThirtyEight, says: “Every day, three times per second, we produce the equivalent of the amount of data that the Library of Congress has in its entire print collection, right? But most of it is like cat videos on YouTube or 13-year-olds exchanging text messages about the next Twilight movie.”

Experiment to find out how much data you need

Machine learning is getting easier and faster. There’s no need to waste a lot of time on preparation, as a huge dataset isn’t a prerequisite. As Adam Savage puts it: “In the spirit of science, there really is no such thing as a ‘failed experiment.’” Simply experiment and see how much data you need.

In the last few years, machine learning and AI tools have been getting simpler and faster. The days of waiting weeks or months for building and deploying models are over. With Akkio, you can build a model in as little as 10 seconds, which means that the process of figuring out how much data you really need for an effective model is quick and effortless.

With traditional machine learning, you typically need a large dataset in order to get sufficient training data. But with Akkio, it’s possible to create compelling models with as little as 100 or 1000 examples. As we’ve explored, if you find that you’re not getting great results with a small dataset, you can always try merging on new data, data augmentation, crowdsourcing platforms, or simply turning to online dataset sources.

Data preparation for machine learning

Preparing your data for the training of a machine learning model can range from simply connecting your existing business operations technology platforms (Salesforce, Marketo, and Hubspot, etc) and data-stores (Snowflake, Google Big Query, etc) to business wide data hygiene programs that take months but yield clean data for optimum performance. You also need to narrow down the dataset used for training so it only has the information available to you when you want to predict a key outcome. We have designed Akkio to work with messy data as well as clean – and are firm believers in capturing 90% of the value of machine learning at a fraction of the cost of a data hygiene initiative. To learn more about preparing your data for machine learning .

Data augmentation for machine learning

The performance of a machine learning model is primarily dependent on the predictive accuracy of its training dataset with respect to the outcome of interest. If you were able to know everything about a system (quantum physics aside) you would be able to perfectly predict its future state. In reality most datasets contain a small subset of information about a system – but that is often more than enough to build a valuable ML model. That said, adding in additional data can often help improve predictive performance. This is called data augmentation. To learn more about data augmentation for machine learning.

Bias in Machine Learning: What is it and how can it be avoided?

One very important thing to be aware of when using machine learning is that biases in the dataset used to train the model will be reflected in the decision making of the model itself. Sometimes these biases are not obvious in your data – take for example zip or postal codes. Location information encodes a lot of information that might not be obvious at first glance – everything from weather to population density to income, housing, to demographics information like age and ethnicity. These patterns can be helpful, but also have the potential to be harmful when the models are used in ways that reinforce unwanted discriminatory outcomes (both ethically and legally).

Use Cases of Machine Learning

Machine learning is a subset of artificial intelligence that is focused on systems that can learn from data.

While we’ll explore some of the top applications of machine learning across a number of industries, the academic world is also using AI, largely for research in areas such as biology, chemistry, and materials science.


Renewable Energy

Renewable energy is one of the fastest-growing sources of power generation worldwide. In 2020, it accounted for 80 percent of new power capacity globally.

AI is critical for successful adoption. AI can balance electricity supply and demand needs in real-time, optimize energy use and storage to reduce rates, and help integrate new, clean sources into existing infrastructures. AI can also predict and prevent power outages in the future by learning from past events.

For example, when a grid is overwhelmed by demand, AI can forecast the trajectory for that grid’s flow of energy and power usage, then act to prevent a power outage. AI can also predict when a power outage will occur in the future, so utilities can take proactive measures to minimize the outage’s effects.

Additionally, AI can even help with wind energy. The power of the wind is ever-present, but harnessing it is not easy. Windmills have been used for centuries to capture wind power, but this process is difficult and costly.

But now AI can change the game. AI can calculate how wind turbines should be rotated so that as few turbines as possible are in the wind shadow of the other. Using data collected from the terrain, the height and size of the turbines, and meteorological data, AI can work out how wind turbines should be rotated to harness the wind.


Insurance Pricing

The insurance industry is highly competitive. The simple fact is that if you are not consistently profitable, you will be driven out of the market. To maintain profitability, insurance firms must be able to accurately predict high-risk, high-cost individuals.

Indeed, data shows that 70 percent of new North American insurance companies fail within 10 years. This is the status quo, as insurance firms often cannot accurately price their plans, leading to tremendous losses.

AI has been shown to be highly accurate when it comes to predicting future claims costs. This accuracy allows you to assess the risk of insuring an individual based on their past claims history and use this information to correctly price your premiums.

This is crucial because it will allow you to stay profitable in a high-risk industry where you are always at risk of being driven out of business by adverse selection.

With Akkio, AI-powered cost modeling can be done in clicks, enabling insurers to leapfrog competitors that are stuck using traditional, laborious, and inaccurate cost models. This cost modeling solves one of the biggest problems insurers face today: Choosing who to insure, and at what rates.

Claim Development Modeling

In the insurance industry, it’s all about risk management. And when you’re making predictions about risk, you want to do it right. In the past, the industry relied on outdated modeling techniques that often led to under- or over-pricing claims. That led to higher premiums for consumers and a host of other problems.

But AI is solving this problem. With these new machine learning techniques, it’s possible to accurately predict a claim cost and build accurate prediction models within minutes. Not only that, but insurers can even build models to predict how claims costs will change, and account for case estimation changes.

That means insurance companies can price their policies more accurately and offer lower premiums for consumers, leading to lower costs of coverage for everyone. It also helps insurers be more competitive and attract more customers, which is especially important as the industry faces stiff competition.

Akkio’s platform makes this possible by enabling users to create models based on their own data, and then deploy them across any number of environments with just a few clicks. This reduces the need for costly and time-consuming custom development work, and translates into lower costs for the company overall.

It also enables insurers to respond faster to a changing insurance market, which provides a critical edge against competitors that are still relying on outdated techniques like regression modeling in Excel. The result is an improved customer experience that translates into higher sales volume and happier shareholders.

Claim Payment Automation Modeling

Claims are a major expense for insurance companies and a frustrating process for policyholders. At the same time, insurance claims are extremely common, as by the age of 34, every person driving since they were 16 are likely to have filed at least one car insurance claim.

The inefficiencies in processing claims is bad for both parties: the customer wastes time and the insurance company spends more on processing than they could have spent on settling the claim. Akkio’s no-code machine learning can model when it’s best to pay off claims automatically, so that you can minimize wait times for customers and maximize ROI for your business.

Predicting when a customer will make a claim is not simple. Your risk profile changes over time, and so does the competitiveness of your market. Given the right historical data, Akkio’s machine learning models take all of this into account, making it easy to find the optimal solution for your specific needs.

Simply upload your data, and let Akkio do the heavy lifting, giving you more time to focus on what really matters: running your business.

Insurance Conversion Modeling

Insurance companies are always searching for new ways to attract new customers, and they need to optimize their marketing efforts to help them grow.

A key problem that many insurance companies are struggling with is how to make accurate pricing decisions. Given that insurance is sold by quoting a policy, accurately estimating the conversion rate from quote to policy is essential. Akkio allows you to gather historical data, make estimates about the probability of conversion, and then use those predictions to drive your pricing decisions.

Accurately modeling insurance conversion is key because it is an important determinant of insurance company profitability.

A key benefit of an AI-based approach is that it allows insurance companies to adjust prices for customer segments without manually creating and testing a wide range of pricing variants. This ensures that marketing dollars are spent effectively and efficiently on segments where there is the greatest chance of conversion.

Fraudulent Claim Modeling

With over $40 billion in insurance fraud in the US alone, according to FBI statistics, it’s no wonder that insurers are looking for ways to reduce fraudulent payouts. One solution is to use machine learning to create models that can predict the probability of a claim being legitimate or not.

Fraudulent claim modeling is an excellent example of how predictive modeling can be used to analyze fraud in the insurance industry. Using a model built on past payouts, an insurer could, for instance, apply a scoring system to claims and automatically reject or flag those with high probability of being fraudulent.

Fraudulent claims don’t just reduce the bottom-line for insurers, they can even lead directly to corporate bankruptcy, as research indicates. Moreover, fraud hurts consumers, who pay up to $700 a year in the form of increased premiums, in the US.

The traditional means of detecting fraud are inefficient and ineffective, as it’s impossible for humans to manually analyze vast amounts of data at scale, which lets fraud slip through the cracks.

Akkio’s potential in this area goes beyond the insurance industry. Modeling fraud is a popular use-case in the financial sector as well, for example to help eliminate fraudulent credit card applications and transactions.

Life Insurance Underwriting for Impaired Customers

Many life insurance companies do not underwrite customers who suffered from some serious diseases such as cancer. This is because it requires them to spend a long and expensive medical assessment process on the customer.

In insurance, the term “impaired” refers to applicants who don’t meet the standard criteria to obtain a very affordable rate. As a result, impaired applicants are often un- or under-insured.

It’s a wise business decision to increase the coverage for impaired customers and Akkio’s AI is able to provide that capability.

While many who suffer from a serious disease can be accurately identified through a questionnaire, Akkio can achieve an even higher degree of accuracy by integrating the applicant’s medical history and conditions. AI-driven predictive models use these factors to predict the risk of underwriting a serious disease survivor. The model predicts the risk of death, which is the ultimate impairment in insurance.

For insurers, it’s possible to build the model in just minutes, opening up a new line of business and boosting the bottom line.

FinTech and Banking

Credit Card Fraudulent Transactions

Credit card fraud is a huge problem costing billions of dollars per year. Fraudulent transactions cost $28 billion in 2018, and they continue to grow rapidly. In fact, annual losses are expected to exceed $40 billion by the end of the decade.

With Akkio’s no-code machine learning, the likelihood of fraudulent transactions can be predicted effortlessly. This reduces the number of fraudulent transactions, while at the same time increases customer satisfaction. For banks, this means less cost per transaction and more revenue and profit.

Akkio’s fraud detection for credit card transactions is one example of how Akkio can help banks. By using a historical transaction dataset, machine learning models identify suspicious patterns and account for factors that are often overlooked in credit card transactions, like IP address changes, high-risk browsing behavior, or a low level of engagement with the transaction.

By using proprietary AI training methods, Akkio can be used to build fraudulent transaction models in minutes, which can be deployed in any setting via API.

Credit Default Rates

Credit default rate is the percentage of loans that default. The credit default rate problem is difficult to model due to its complexity, with many factors influencing an individual’s or company’s likelihood of default, such as industry, credit score, income, and time.

Understanding the factors that lead to credit card defaults can help lenders better assess the risk of lending to borrowers, and ultimately boost the bottom-line. Credit risk is a measure of the likelihood that a person will be unable to repay a debt, and this is what lenders use to determine whether or not to offer credit. In finance, credit risk is the risk of default on an obligation that arises due to the uncertainty of future cash flow.

Akkio’s API can help any organization that needs accurate credit risk models in a fraction of the time it would take to build them on their own. Akkio makes it easy to build a model that predicts the likelihood of default based on data from the past.

In addition, Akkio can be used for automatic model retraining, so that once a model is built, it’s easy to maintain and update as needed. This makes it possible for organizations not just to save time on predictive modeling tasks but also to be confident in their models at all times.

Digital Wealth Management

Digital Wealth Management is a competitive field. In this market, it’s not just about having the best investment products, but also about how to distribute them effectively while managing client assets. Akkio’s machine learning algorithms can be deployed to constantly analyze data from your existing clients’ portfolios to find new opportunities and assign values for each of your prospects.

It’s important to diversify your portfolio to make sure you are investing in the right technologies and companies. AI can help diversity portfolios by finding new investment opportunities

Akkio helps asset managers learn which customers are more likely to invest in particular categories based on their previous investments and demographic information, as well as information like their risk appetite.

AI can even be used to automate investment analysis, by ingesting financial data from sources like a securities market to predict the probability of stock prices rising or falling. These predictions can then provide real-time strategy recommendations for individuals or institutional investors.

The result? A successful asset management strategy that attracts new clients and captures a greater share of existing client assets at the same time.

Further, algorithms have been used in stock trading for decades. For example, a 1986 New York Times article titled “Wall Street’s Tomorrow Machine” discussed the use of computers for evaluating new trading opportunities.

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Today’s AI trading is a form of automated trading that uses algorithms to find patterns in the market and make trades. AI traders can also be used to optimize portfolios with respect to risk and return objectives and are often used in trading organizations.

AI-powered trading systems can also use sentiment analysis to identify trading opportunities in the securities market. Sophisticated AI algorithms can find buy and sell signals based on the tone of social media posts.


A blockchain is a decentralized database that stores information in blocks of data. The blocks are linked together through cryptography to create a history of all transactions. The system relies on consensus among the users of the network about the validity of information and data, making blockchains more secure than other types of databases.

However, as blockchain technology becomes more popular, security threats are also increasing. Larger blockchains like Bitcoin and Ethereum are practically impossible to attack due to the sheer amount of resources required. That said there are hundreds of smaller blockchains at risk.

MIT Technology Review reports, “marketing slogans and headlines that called the technology ‘unhackable’ were dead wrong,” as blockchains can be rewritten if an attacker is able to muster over 51% of the computational power defending a network allowing the attacker to reallocate ownership of funds. One such example is when Ethereum Classic (a fork off of Ethereum) suffered a 51% attack 3 times in a single month. In 2020, there were over 120 blockchain attacks, leading to losses to the tune of nearly $4 billion.

While preventing 51% attacks depends on distributed participants allocating compute resources to chain defense, users and exchanges need to be able to detect anomalous behavior when it happens on a chain (so they can attempt to minimize loss of funds).

Akkio’s machine learning algorithms can detect anomalies in real-time, alerting you and enabling you to take action quickly before additional damage is done. With Akkio’s AutoML, it only takes minutes to build a fraud detection system tailored to your needs.


Drug Delivery Optimization

The pharmaceutical supply chain is notoriously fragile, leading to shortages, higher costs, and safety issues. Part of these issues lie in under-optimized drug delivery systems.

Pharmaceutical firms spend millions of dollars shipping drug samples to doctors and hospitals. Simple analyses uncover situations for order consolidation, such as when the same location requests two or more drug samples. However, manually looking at the data for order consolidation quickly becomes infeasible at scale.

AI helps optimize supply chain delivery processes by predicting which orders can be consolidated, no matter how complex or how many orders there are to process. That’s the killer advantage of AI: It’s incredibly fast and accurate compared to traditional techniques.

AI can be used to find the best locations for consolidated shipping, estimate cost savings, and improve customer satisfaction. Instead of putting out fires related to unoptimized supply chains, health systems can now focus on what truly matters: Helping patients.

Disease Propensity

In a world of virtually unlimited data and powerful analytics, it’s easy to see why health systems are looking for ways to better understand the health of their patients. With AI platforms, teams can connect to various data sources, like lab results and HIE, and use machine learning models to predict the severity of a patient’s condition and what type of care they will need.

Medical professionals should consider screening patients that may have a higher likelihood for a particular disease. If they see a patient that could be predisposed to developing an illness, treating them right away will lead to better health outcomes, in addition to being more fiscally responsible than not seeing them until they’re carrying it.

Ultimately, using AI to automate disease propensity modeling has the potential to save hospitals and other healthcare providers millions of dollars per year by reducing unnecessary emergency room visits and readmissions.

Modeling ICU Occupancy

Staffing and budgeting for a hospital ICU is always a difficult decision, and it’s even harder when you don’t know how quickly the patient load will change. With machine learning, hospitals can easily make projections about their occupancy by modeling historic data to account for trends.

Exceeding capacity limits, as has happened in ICU rooms around the world as of late, often results directly in patient death. Higher occupancy rates are clearly correlated to higher death rates.

With AI, hospitals can quickly create a model that forecasts occupancy rates, which consequently leads to more accurate budgeting and staffing decisions. Machine learning models help hospitals save lives, reduce staffing inefficiencies, and better prepare for incoming patients.

Forecasting models also help hospitals make better decisions about what services they need to offer their patients. Healthcare has been rapidly changing over the last few years, with an increased focus on providing holistic care and individualized treatment plans. Further, forecasting can help hospitals anticipate patient needs and provide the right services to meet expectations.

Ultimately, machine learning algorithms make it easy for hospitals to predict the next step in their operations and make more informed decisions about future staffing needs. The result is healthier, happier patients and a stronger bottom line for hospitals.

Estimating Sepsis Risk

Sepsis is a life-threatening condition that can develop suddenly and with devastating consequences. It is a leading cause of death in intensive care units and in hospital settings, and the incidence of sepsis is on the rise. Doctors and nurses are constantly challenged by the need to quickly assess patient risk for developing sepsis, which can be difficult when symptoms are non-specific.

Decades ago, sepsis wasn’t much of a concern. Today, sepsis accounts for almost a fifth of human deaths.

AI complements medical professionals’ expertise by providing data-driven insights to identify patients at high risk for developing sepsis. Medical professionals can leverage the power of machine learning to aggregate patient data and generate automated alerts tailored to each patient’s unique needs.

Machine learning models are designed to learn from historical data, which can include past sepsis cases, to provide accurate predictions, enabling healthcare professionals to confidently identify patients at high risk for developing sepsis.

Hospital Readmission Risk

The average cost of a hospital readmission ranges from $15,000 to $25,000, which leads to wasted resources, unnecessary tests, potentially harmful treatments, delayed patient care, and other damaging consequences.

Machine learning can help in reducing readmission risk via predictive analytics models that identify at-risk patients. By feeding in historical hospital discharge data, demographics, diagnosis codes, and other factors, medical professionals can calculate the probability that the patient will have a readmission.

AI makes it easy for hospitals to identify which patients are most at-risk for readmissions. No-code AI tools don’t require any IT work or coding, so hospitals can save money and improve the quality of care they provide.

Ultimately, AI’s hospital readmission risk use-case can help hospitals reduce their costs and increase the quality of care they are able to provide to their patients.

Public Sector


Terrorism is a top concern for intelligence and law enforcement agencies around the world. After 9/11, preventing terrorist attacks became a heavily-funded, prime directive for a number of government agencies.

As described in a United Nations Office of Counter-Terrorism report on AI, government agencies can use predictive modeling to identify red flags of radicalization, detect the spread of terrorist misinformation, and counter terrorist narratives.

Machine learning isn’t just for marketing; it can also be used to help prevent terror attacks by identifying patterns in past events and predicting future ones, saving lives, and making the world a safer place.

Fraud detection

Fraud is an issue that is costly, not only to the government and its citizens, but to companies as well. Every government agency from the IRS to the Social Security Administration suffers significant losses from fraud.

In fact, as explored in an Association of Certified Fraud Examiners report, a study of nearly 3,000 cases of occupational fraud found that government entities “were the most represented sectors among the fraud cases analyzed.” While much public discourse centers around governments as perpetrators of fraud, the reality is often that government employees and agencies are often the targets of a wide-range of fraudulent activities.

Fraudulent activities can be difficult to detect, costing agencies valuable time and resources. Ultimately, AI makes it easy for government agencies to detect fraudulent activities as they happen, saving them time and resources while also safeguarding taxpayer dollars.

Insider threat

In the age of digital transformation, attack vectors are getting ever larger. As a result, even government agencies are at risk of being breached by insiders (or ex-employees) who want to use their data for malicious purposes.

At the same time, there are a number of insider threats that can seem innocuous in nature, but costly nonetheless, such as sending company information over a personal account, or even accidentally misconfiguring access credentials.

For example, while cybersecurity firms like to keep their exact techniques private, research shows that AI can accurately identify malicious emails, which cost governments billions of dollars if undetected.

To make sure that firms don’t have to pay for these kinds of internal breaches, agencies need to proactively block any potential misuse, using machine learning to identify risks.


Cyberattacks are on the rise, with real-world consequences for everyday people. Recently, for instance, hackers stopped gasoline and jet fuel pipelines and closed off beef and pork production at a leading US supplier. These are just a couple of examples of the tens of thousands of annual cybersecurity attacks.

One of the main challenges in cybersecurity today is an ever-growing attack vector. As more and more of our world goes digital, there’s more data to keep track of, and it’s easier for hackers to go unnoticed. Manually combing through this data can only get you so far, but AI can scan massive amounts of data in real-time.

No-code AI enables security teams to build, deploy and refresh models to predict incoming threats in real-time, whether it’s scanning incoming emails for malicious threats or flagging concerning IP activity, so they can prevent a breach before it happens.

Ultimately, this enables security teams to reduce their risk exposure and prepare for an increasingly hostile cyber landscape. Teams that fail to deploy AI for cybersecurity will be more vulnerable to attacks compared to other market players who do.

Customer Support

Support Ticket Topic Classification

Good customer service is of universal importance, with surveys indicating that 96% of customers feel customer service is important in their choice of loyalty to a brand.

Customer service is also a major factor in customer retention. In other words, people are more likely to stay with a company if they’re satisfied with the service they receive.

AI-based classification of customer support tickets can help companies respond to queries in an efficient manner. By combining natural language processing and machine learning, AI can be used to automatically group queries into predefined categories, making it easy for customer support teams to select the appropriate department to handle a query based on their area of expertise.

Essentially, by digesting past queries to find patterns in terms of content, AI can learn how to classify new tickets more accurately and efficiently. This means that with time, AI-based ticket classification will become an integral part of any organization’s customer service strategy.

Support Ticket Prioritization

Customer support teams need to handle a huge number of customer queries in a limited time, and they’re often not sure which tickets need to be addressed first. Machine learning models can rank tickets according to their urgency, with the most urgent tickets addressed first. This relieves teams of the burden of deciding which tickets require the most attention, freeing up more time for actually addressing tickets and satisfying customers.

Predictive analytics is also useful for identifying patterns in the data so that customer queries can be more accurately met with answers, and it allows teams to improve their customer experience by responding faster.

Social Media Sentiment Analysis

Social media is an invaluable tool for marketing and customer support teams, but it’s a complicated and fast-moving landscape. Every day, millions of people post their thoughts, opinions, and suggestions to social media about brands they’re interacting with. From a raving comment to a scathing review, social media posts can have a big impact on your company’s success.

Machine learning can help teams make sense of the vast amount of social media data, by automatically classifying the sentiment of posts in real-time thanks to models trained on historical data. This enables teams to respond faster and more effectively to customer feedback.

Ultimately, this allows marketers and customer service teams to identify early warning signs of dissatisfaction before they spiral out of control and needlessly drive away customers.


Finding Duplicate Customer Records in Your Database

In the process of data entry, we know that errors will be made. Humans are not perfect and this includes those who code the data: editing mistakes can occur such as inverting an “S” or a “Z” in the input document. It is reasonable to assume that there may be multiple copies of your records in which different people may have typed one letter wrong or did not notice inconsistent formatting, such as “smith” versus “Smith,” before saving it as a new version.

Additionally, data can be brought in by multiple systems, with different column values, such that duplicates won’t be found by traditional means (e.g. one system has the first and last name, while another system has their email).

Detecting duplicates is notoriously difficult, requiring manual intervention to identify duplicate records. This can be time-consuming and prone to human error. AI is different: it’s fully automated and can detect duplicates for all types of fields with high accuracy.

AI is essential for complex deduplication tasks, because the same record could show up multiple times throughout your database. With AI, you can detect these duplicates even if they have different data fields – making it easy to clean up your database so that it adheres to best practices without any manual intervention.

Lead Scoring

Lead scoring is a powerful way to determine which leads are most in need of your attention. AI enables teams to automatically predict the likelihood that each lead will become a paying customer. Armed with these insights, marketing teams can decide which leads to pursue and spend time on, and which to put on the back-burner.

Today’s lead scoring is powered by machine learning that leverages any historical data, whether from Salesforce, Snowflake, Google Sheets, or any other source, to predict the likelihood a given lead will convert.

This insight helps marketing teams to identify leads that are in need of more attention, as well as those that are likely to be a waste of time for the team.

Sales Forecasting

As a business, forecasting is one of your most important tasks. It’s what allows you to plan ahead and make better use of your budget.

Machine learning can help you do that with unparalleled accuracy, even in unpredictable economic environments. No-code AI can be used to quickly build a model from past sales data and predict the sales you’re likely to receive in the future. With no-code AI, you can get accurate forecasts in a matter of seconds by uploading your product catalog and past sales data.

Instead of relying on rules of thumb or gut feelings, AI offers a more scientific approach that lets you make better decisions about your budget, staff hiring, and promotional campaigns.

This is essential for businesses that need to know how to budget for the future or optimize their limited resources. Forecasting models can be deployed through a web-based interface, API, Salesforce, or even through Zapier, making it easy to get started in any setting without requiring any data science know-how.


Direct Marketing

The way we consume goods has changed. In the past, we would go to the store, pick out what we needed, and purchase it. Nowadays, we can order what we need from the comfort of our own home and have it delivered to our door.

As a result, the way we are marketed to has changed. Direct marketing is an excellent way for businesses to reach their potential customers, and it’s a largely under-utilized opportunity.

That said, it’s often difficult to determine which prospects are the most likely to purchase. Marketing to uninterested leads isn’t just a waste of time and money – it can be a huge turn-off to those leads from ever deciding to make a purchase decision.

That’s where data-driven AI comes in.

AI can find the best prospects among a particular group and determine the best way to reach them. This means you can quickly and easily identify the most valuable leads, and then contact them with a personalized message that speaks to their particular needs.

With no-code AI, you can effortlessly prioritize and classify leads based on their likelihood of converting, all at a fraction of the time and cost that traditional methods require.

Loyalty Program Usage

A loyalty program is a reward program that gives points or other awards to customers who shop at a particular establishment. A typical example might be a program that provides each customer with ten points for every dollar spent at the store, and if a customer collects 1,000 points, they are given $10 off their purchase.

Loyalty programs are designed to incentivize customers to shop with the company on a regular basis, and they usually consist of various tiers of rewards, depending on how much the customer spends each time. The most effective type of loyalty program is one that provides increased benefits based on the amount of money spent, as customers are more likely to be motivated by the prospect of an increased reward.

Unfortunately, even if you have a good understanding of your customers’ behaviors and preferences, it is not easy to predict which rewards will incentivize them most effectively. While your neighborhood coffee shop might offer a free coffee for every fifth visit, the scale and complexity of loyalty programs are orders of magnitude greater for large, data-driven firms.

Machine learning algorithms can analyze past data and detect which customer segments are most likely to respond positively to certain rewards. This helps managers make informed decisions about which rewards to offer and when, increasing the likelihood that they will convert.

Next Best Offer

One of the best ways that marketers can create a personalized experience for customers is by considering the “next best offer.” This requires marketers to take into account all of the possible actions they could take with that customer and then select the most appropriate one.

As an example, suppose that a customer visits a website for information on renting. The customer can’t decide between a studio or one-bedroom apartment, so she searches for more information on both and cannot find any definitive information. In this case, the “next best offer” could be to create a personalized email with links to articles and videos from both types of apartments, so the customer can decide which one is better for her.

Doing this manually is clearly impossible at scale. Businesses can use AI to offer the right product to the right person at the right time.

Businesses can automatically make recommendations in real-time, using predictive models that account for customer preferences, price sensitivity, and product availability, or any data provided for training.

Predicting the right offer for the right person at the right time is a huge undertaking, but AI makes it easy for retailers to optimize their operations. Best of all, retailers don’t need any data scientists or AI specialists to deploy predictive models – no-code AI automatically powers recommendations with no coding required.

Multichannel Marketing Attribution

If your marketing budget includes advertising on social media, the web, TV, and more, it can be difficult to tell which channels are most responsible for driving sales. With machine learning-driven attribution modeling, teams can quickly and easily identify which marketing activities are driving the most revenue.

Marketing attribution models are traditionally built through large-scale statistical analysis, which is time-consuming and expensive. No-code AI platforms can build accurate attribution models in just seconds, and non-technical teams can deploy the models in any setting.

This lets marketing teams keep costs down while still pinpointing exactly where to allocate their marketing budget to optimize for the best ROI. Ultimately, this makes it easier to ensure that every dollar spent on marketing is worth it, so you’re consistently getting the most out of your marketing budget.

By automating attribution, marketers can overcome the boring stuff and get more creative with what really matters. Armed with knowledge on how specific channels are performing, marketers can finally double-down on high-performing channels, eliminate the laggards, and strategize how to move forward.

Product Personalization

Consumers today expect personalized products and content.

Machine learning enables businesses to finally target consumers with the right message, at the right time, and on the right channel.

For example, rather than using one message to reach everyone on your website, machine learning could be used for sentiment analysis of customer reviews on your site, or your CRM or social media tools, to present different customer segments with different messages.

In addition, AI platforms can be trained on historical product purchase data to build a product recommendations model. For example, if a customer has purchased a certain product in the past, an AI API can be deployed to recommend related products that the customer is likely to be interested in.

This can be a powerful propellant for the bottom line, as research shows that 80% of consumers are more likely to make a purchase when brands offer personalized experiences.

Beyond personalized experiences, AI can even be used for personalizing products and services themselves.

While today, many of these individualized products are created by an individual designer or a custom order, personalized AI will make this process much more efficient, tailoring the product to an individual customer’s needs and delivering it in a matter of days.

Customer Churn

The churn rate, also known as the rate of attrition, is the number of customers who discontinue their subscriptions within a given time period. For a company to grow, it must acquire more new customers than its churn rate.

It’s quite a challenge to prevent customer churn, which is why it’s so important for companies to be proactive.

Fortunately, AI has the power to do just that. Machine learning algorithms can identify the data patterns common among customers who are likely to churn, such as those with a high cost of acquisition or those that are misaligned with your ideal customer persona.

Armed with this knowledge, you can optimize your retention strategy by targeting high-risk customers with personalized offers or incentives before they leave. Moreover, marketing teams can tailor their strategies to avoid high-churn-profile leads.

The more data you have, the better. AI platforms like Akkio allow you to work with your data sources wherever they are – your CRM system, data warehouses, and other databases – to create the best model for predicting churn for your business.

Next Best Action

When it comes to marketing, there are always more tactics to explore than time or resources allow. Trying to decide which channel or activity to focus on that will have the biggest impact on revenue means you’re forced to make guesses.

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AI can put those guesses to the test. Machine learning algorithms can be fed with data from all of your marketing channels, as well as customer lifecycle information, to identify which activities are most likely to move each individual customer closer to purchase.

A/B testing is a great way to figure out how best to allocate marketing resources, but only if you can measure success accurately. That’s where machine learning excels: it’s able not only to measure and predict sales, but also predict what might happen if you try any given marketing tactic.

Google AdWords Bidding

Google AdWords is a huge part of most advertising budgets, but it can be difficult to get bidding right. If you bid too low, you lose out on opportunities. If you bid too high, your marketing ROI will dwindle.

However, machine learning can make this process easier by building a model off of past marketing and sales activities to predict the sales volume attributable to each AdWord, making it easy to determine the optimal price to bid to achieve your target ROI while avoiding losing the word to a competitor.

It is incredibly difficult and time-consuming for teams to build auction models that can capture complex human behavior. But no-code AI can be used to build accurate models with just a few clicks. Companies can deploy these models easily with an API in any setting or even with no-code tools like Zapier.

Ultimately, this enables marketing teams to boost the effectiveness of their ad spend, which is critical for success in an ever-more competitive landscape for consumer attention. Teams that fail to deploy AI for AdWords bidding will lose directly to their competitors that are using data-driven strategies.

Lead Scoring

Lead scoring is a crucial part of any marketing campaign because it helps you focus your time and resources on the potential customers that are most likely to become paying customers. In other words, an accurate lead scoring model helps you go where the money is. In fact, over two-thirds of marketers point to lead scoring as a top revenue contributor.

Accurate lead scoring can be tough, though. It’s not easy to measure how well a customer will interact with your product without knowing much about them, so traditional lead scoring models rely on interest from the prospect to determine the score. Traditional approaches are highly limited, since they don’t necessarily indicate the prospect’s ability or true probability of making a purchase.

That’s where AI comes in. Machine learning models use a wide range of factors to score marketing leads. With data-driven lead scoring models, you can have more confidence in your marketing decisions because you’re looking at more data points than just interest from the prospect.

Employee Retention

Studies have shown that attracting and retaining top talent is one of the most important factors in a company’s success. After all, the average employee exit costs an entire third of their annual salary.

However, as employee-employer relationships are shifting, the challenge of getting and keeping top talent is getting tougher. Year after year, employee attrition is increasing, and some are calling this crisis “The Great Resignation.”

But there’s hope: data. No-code AI platforms let HR professionals scan massive amounts of data – from hiring pipelines to employee history or performance reviews – to uncover insights to keep your best people working for your team.

With no-code AI, you can use machine learning algorithms to create predictive models that let you predict when an employee might be considering a job change, when they might be considering leaving their current position, or if they’re simply unsatisfied.

This data-driven approach illuminates potential issues before they become major problems, giving HR teams the high-quality insights they need for more informed decision-making. With tools like Zapier, HR teams can even deploy predictive models in any setting without writing code.

How can I create and deploy a machine learning model?

For many, machine learning might as well be magic. But the truth is, as we’ve seen, that it’s really just advanced statistics, empowered by the growth of data and more powerful computers.

Having said that, machine learning models are incredibly versatile tools that can add tremendous value across business units. We saw earlier, for example, how finance teams can use machine learning to predict fraud, marketing teams can score leads or predict churn, HR teams can predict attrition, and more.

Building the machine learning models to make these use-cases possible was once an arduous, resource-intensive task, requiring technical experts for data engineering, building pipelines, coding, maintaining infrastructure, and more.

As we’ve explored, no-code AI allows anyone to create and deploy machine learning models on their own, without needing programming skills. However, to become truly AI-driven, getting AI to work for you is not a one-time upgrade. It is a journey that will require an understanding of data management and the use of machine learning.

Another reason that code-based AI is problematic is that there is a shortage of programmers, and the shortfall is expected to grow as the AI industry grows. As ACM reports, there’s actually a recent decrease in computer science graduates, in spite of increasing demand for them, fueled by delays in student visa processing, limited access to educational loans, and travel embargos.

Start with data

As we’ve seen, data is the fuel that powers machine learning engines, which is why data preparation is so important when building a model.

The expression “the more the merrier” holds true in machine learning, which typically performs better with larger, high-quality datasets. With Akkio, you can connect this data from a number of sources, such as a CSV file, an Excel sheet, or from Snowflake (a data warehouse) or Salesforce (a Customer Relationship Manager).

For example, suppose you’d like to use AI to score sales leads. If your business uses Salesforce, you can directly connect your sales dataset, and then select a column that relates to whether or not a deal was closed.

Many smaller sales teams keep it simple, using Google Sheets or Excel to organize lead data. Both of these sources can be easily connected to Akkio as well, and you’d build the model in the same way—by selecting the column you’d like to predict.

On the other end of the spectrum, some larger firms use Snowflake for handling massive amounts of sales data, which can be easily integrated with Akkio as well.

Train a model

We’ve explored how machine learning models are mathematical algorithms that are used to find patterns in data. To train a machine learning model, you need a high-quality dataset that is representative of the problem you’re trying to solve. Let’s walk through a practical example.

In Akkio, you can train a model by hitting “Add Step” once a dataset is connected, and then “Predict.” Then, simply select the column to predict.

Generally speaking, there are two kinds of models you can train: Classification models and regression models.

A few examples of classification include fraud prediction, lead conversion prediction, and churn prediction. The output values of these examples are all “Yes” or “No,” or similar such classes.

On the other hand, regression models are used to predict a range of output variables, such as sales revenue or costs.

After selecting “Predict,” training either kind of model is the same: You’ll select the column name you want to predict, whether it’s called conversion, churn, attrition, fraud, or any other metric. You also have the option to select a “Training Mode,” which ranges from 10 seconds of training time to 5 minutes, where longer training times may lead to more accurate models.

Behind the scenes

While the training process is done in just a couple clicks, a lot of work is done in the background.

It starts with software engineering to lay the groundwork for the platform itself. Software engineering is a branch of engineering that deals with the design, development, operation, and maintenance of software. Most of today’s software development activities are performed by a team of engineers.

But that’s not all. DevOps is used to help bring AI applications to production.

DevOps is a software development method that focuses on the collaboration between software developers and other IT professionals. It aims to shorten the time between the software’s conception and its adoption by end users.

In order to build the AI pattern recognition models themselves, a number of different approaches are used. Pattern recognition is the ability to identify a pattern in data and match that pattern in new data. This is a key part of machine learning, and it can be either supervised or unsupervised.

The Bayesian approach to AI is a probabilistic approach to making decisions. Bayesian methods are used to estimate the probability of a hypothesis, based on prior knowledge and new evidence.

Another technique is dimensionality reduction, a process that reduces the number of dimensions of a dataset by identifying which are important and removing those that are not.

K-means clustering and PCA, or Principle Component Analysis, are two methods commonly used together. In order to group associated data points, k-means finds the partition in data, while PCA finds the cluster membership vector.

Random forest is another common method. A random forest is a machine learning method that generates multiple decision trees on the same input features. The hierarchy of decision trees is built by randomly selecting observations to root each tree.

Gradient descent is a commonly used technique in various model training methods. It’s used to find the local minimum in a function through an iterative process of “descending the gradient” of error.

These AI methods are often built with tools like TensorFlow, ONNX, and PyTorch.

TensorFlow is an open-source software library for Machine Intelligence that provides a set of tools for data scientists and machine learning engineers to build and train neural nets. It is one of the most popular deep learning frameworks.

ONNX is an open-source modeling language for neural networks that was created to make it easier for AI developers to transfer their algorithms between systems and applications. This open-source AI framework was made to be widely available to anyone who wants to use it.

PyTorch is an open source machine learning library for Python, based on Torch. PyTorch provides GPU acceleration and can be used either as a command line tool or through Jupyter Notebooks. PyTorch has been designed with a Python-first approach, allowing researchers to prototype models quickly.

All of these model training processes are iterative, and many technical model training considerations are accounted for.

One of these concerns is overfitting, which happens when a model tries to predict every individual input that it might get instead of just being able to predict certain patterns in the data.

There are best practices that can be followed when training machine learning models in order to prevent these mistakes from happening. One of these best practices is regularization, which helps with overfitting by shrinking parameters (e.g., weights) until they make less impact on predictions. An additional best practice for successful training is using cross validation.

Another concern is called the “curse of dimensionality.” This happens when the number of inputs to a model gets too large for it to work properly, especially if many inputs are not statistically relevant to the outcome being predicted. A way to get around this is by simplifying or reducing the number of features or dimensions being used in order to make more accurate predictions – this is known as “dimensionality reduction.”

One technique for dimensionality reduction is called Principal Component Analysis, or PCA. PCA turns a large amount of data into a few categories that are most useful for describing the properties of what you’re measuring.

Evaluate model performance

Not all machine learning models are made equal. There’s a popular saying in the AI world: “Garbage in, garbage out.” If low-quality data is used to build a machine learning model, then the model will generate low-quality predictions as well.

There are a number of metrics you can use to evaluate the performance of a model. After making any model in Akkio, you get a model report, including a “Prediction Quality” section.


If you’ve built a classification model, the quality metrics include percentage accuracy, precision, recall, and F1 score, as well as the number of values predicted correctly and incorrectly for each class.

Here are what these fields mean:

  • Accuracy: Accuracy measures how often a prediction is correct, and is calculated by dividing the number of correct predictions by the total number of predictions.
  • Precision: Precision is the fraction of true positives out of the predicted positives. This is useful to consider when the cost of a false positive is high, such as in email spam detection. If an important email is incorrectly classified as spam, you’ll lose important information.
  • Recall: Recall is how many of the actual positives your model captures. This is useful to consider when the cost of a false negative is high, such as in malignant cancer prediction.
  • F1 Score: The F1 score combines precision and recall into one metric and weights them, in order to balance the consideration of false positives and false negatives.


Because forecasting is used to predict a range of values, as opposed to a limited set of classes, there are different evaluation metrics to consider.

After building a forecasting model, such as for cost modeling, you’ll see an RMSE value and a field called “usually within.”

RMSE stands for Root Mean Square Error, which is the standard deviation of the residuals (prediction errors). The “usually within” field provides values that are simpler to understand in context, such as a cost model that’s “usually within” $40 of the actual value.

Deploy a model and make predictions

VentureBeat reports that 87% machine learning models never make it into production. This is affirmed by a separate study indicating that just 14.6% of firms have deployed AI capabilities in production.

We can’t blame them. AI is a difficult task, and many companies try to reinvent the wheel by building their own data pipelines, model infrastructure, and more. At the same time, a McKinsey survey found that just 8% of respondents engaged in effective scaling practices. What this means is that many firms are building models, but are unable to deploy them, particularly at scale.

With Akkio, businesses can effortlessly deploy models at scale in a range of environments. More technical users can use our API to serve predictions in practically any setting, while business users can deploy predictions directly in Salesforce, Snowflake, Google Sheets, and thousands of other apps with the power of Zapier.

The term API is short for “application programming interface,” and it’s a way for software to talk to other software. APIs are often used in cloud computing and IoT applications to connect systems, services, and devices.

By querying Akkio’s API endpoints, businesses can send data to any model and get a prediction back in the form of a JSON data structure.

For context, data structures refer to the way data is organized in a computer program. Data structures are built on two concepts: data types and data manipulation. Data types define the type of data in the structure, such as number, word, or image. Data manipulation defines how data is organized in the structure, such as linear, hierarchical, or tree.

Models can even be deployed via web app to instantly get a URL to share with others. When you hit “Deploy” for a web app, you’ll also get an iFrame embed (an inline frame), which is an HTML tag that can be embedded in any site.

Users who deploy models can take advantage of cloud storage that scales to accommodate unlimited data uploads. AI is the next growth engine for cloud storage, with a massive annual growth rate.

Further, these cloud servers are home to huge Graphical Processing Unit (GPU) clusters. AI algorithms that require a lot of mathematical calculations, such as neural networks, are well suited to GPU processing, such that cloud servers enable unlimited scalability of model predictions.

Continuous Learning (what it is and why it matters)

The importance of continuous learning in machine learning cannot be overstated. Continuous learning is the process of improving a system’s performance by updating the system as new data becomes available. Continuous learning is the key to creating machine learning models that will be used years down the road.

The process of updating a system with new data, or “learning”, is something that is done by people all the time. Continuous learning seeks to replicate this process in a machine. The key to building robust models that continue to be valuable in the future is to learn from new information as it becomes available. This would allow the machine to adjust its behavior accordingly when responding to new information, just like humans do.

The more data a machine has, the more effective it will be at responding to new information. The extent to which continuous learning is applied will help determine how intelligent the system is and how well it responds to new situations.

ML Operations

Machine Learning Operations (MLOps) is the compendium of services and tools that an organization uses to help train and deploy machine learning models.

MLOps services help businesses and developers to get started with AI, with service offerings that include data preparation, model training, hyper-parameter tuning, model deployment, and ongoing monitoring and maintenance. Organizations with a large training pipeline need MLOps to efficiently scale training and production operations.

These services allow developers to tap into the power of AI without having to invest as much in the infrastructure and expertise that are required to build AI systems.

With Akkio, machine learning operations are standardized, streamlined, and automated in the background, allowing non-technical users to have access to the same caliber of features as industry experts.

Data Preparation

To recap, data preparation is the process of transforming raw data into a format that is appropriate for modeling, which makes it a key component of machine learning operations. This process typically includes splitting the data into parts for training and validation, and normalizing the data.

This means randomly splitting the data into a set of two subsets, known as “training data” and “testing data” (this is called stratified sampling). The first subset is then trained to try and find patterns in the data, but the model doesn’t know what’s coming next. The second subset is used as new input the AI has never seen before, which helps better predict outcomes.

That way, when you create predictions on new inputs using this model, they’re more accurate, because you’re using examples that have not already been seen by the model.

Data preparation can also include normalizing values within one column so that each value falls between 0 and 1 or belongs to a particular range of values (a process known as binning).

For example, if someone were providing demographic information about people who visit their website and are able to purchase goods online, it would be helpful to split them into male or female; under 18 or over 18 years old; and so on, in order to classify their behavior while browsing based on these groupings.

Model Training

The training phase is where machine learning models are generated out of algorithms. The algorithm may determine which features of the data are most predictive for the desired outcome. This phase can be divided into several sub-steps, including feature selection, model training, and hyperparameter optimization.

The goal of feature selection is to find a subset of features that still captures variability in the data, while excluding those features that are irrelevant or have a weak correlation with the desired outcome.

Machine learning algorithms are supported by inferential statistics to “train” the model, such that it is able to make “inferences” about new data.

Machine learning will often operate via a feedback loop whereby input data starts with an empty algorithm, which then finds patterns in that data over the course of multiple iterations. That information is fed back into the algorithm which modifies its parameters and goes through another iteration for refinement, until the optimal model is found.

Finally, hyperparameter optimization determines what set of hyperparameter settings should be used based on some criteria, such as cost or computational efficiency. Factors to consider when evaluating model hyperparameter tunings can include:

  • Accuracy vs speed tradeoff
  • The degree of robustness against overfitting and underfitting due to a large number of tunable parameters vs accuracy tradeoff

Model Deployment

The process of deploying an AI model is often the most difficult step of MLOps, which explains why so many AI models are built, but not deployed.

There are a number of different considerations to plan for, including: How will data be queried? What product or service will the AI model be embedded into? How do we ensure that all pieces of the model will continue to work together as expected over time?

These are just some of many questions which must be addressed before deployment. With Akkio, teams can deploy models without having to worry about these considerations, and can select their deployment environment in clicks.

Nowadays, there are many creative ways to deploy AI. For instance, you can deploy models on mobile phones with limited bandwidth, or even offline-capable AI servers. Offline AI is a model deployment option that can be used to serve predictions locally, or “at the edge,” for use-cases like smart CCTVs that might be in a wireless dead zone, or even AI-powered medical diagnostic apps that deal with sensitive health data.

Our Learners Also Ask:

1. Can you list the top four machine learning challenges?

Machine learning faces four basic difficulties: struggling to maintain the data (using a model that is too complex), underfitting the data (using a model that is too simple), data scarcity, and unrepresentative sample data.

2. What questions ought I put to machine learning?

Top Interview Questions for Machine Learning

  • What Kinds of Machine Learning Are There?
  • What is overfitting and how can it be prevented?
  • In a machine learning model, what do the terms “training Set” and “test Set” mean?
  • How Should Missing or Invalid Data Be Handled in a Dataset?

3. What does a machine learning cheat sheet mean?

Choosing the ideal algorithm from the developer for a predictive analytics model is made easier with the Azure Machine Learning Algorithm Cheat Sheet. A vast library of algorithms from the classification, recommender systems, clustering, outlier detection, regression, and text processing families are available in Machine Learning.

4. What fundamental ideas underlie machine learning?

Supervised learning and unsupervised learning are the two primary subfields of machine learning. These two notions are more closely tied to what we want to accomplish with the data, despite the fact that it may look like the first pertains to prediction with human involvement and the second does not.

5. What does machine learning bias mean?

What does machine learning bias mean? The phenomenon of bias skews an algorithm’s output in favor of or against a certain idea. The model of machine learning itself experiences bias as a result of false assumptions made throughout the ML process.

6. How does machine learning work?

Simply defined, machine learning enables users to send massive amounts of data into computer algorithms, which then analyze, recommend, and decide using only the supplied data.