Machine Learning Courses In AI

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Professionals with machine learning skills are in high demand. With every industry applying AI in their domain, studying machine language is the right way to take your career to the next level. Whether you’re a newbie in the workforce, early professionals looking to change career paths, or seasoned professionals looking to stay on top of things – upskilling in machine language can be the next best thing you do.

Simplilearn’s Skillup is a platform for learning today’s in-demand skills for free. Our courses provide a world-class learning experience that helps build strong foundational skills for career growth. These courses can help you:

  • Gain new skills for free.
  • Get trained by qualified instructors and industry experts.
  • Learn at your own pace.
  • Benefit from our free guides on career paths, salaries, interview tips, and other valuable resources.
  • Pick from more than 600 sought-after skills across various in-demand domains.
  • Add an industry-recognized Completion Certificate to your resume.

We have curated for you the Top 9 Free Machine Learning Courses To Fast-Track Your Career In 2023.

Choose the certification that best suits your goal.

1. Machine Learning Basics

Learn the basics of Machine Learning with our free Machine Learning course that provides a solid foundation and key skills to help machine learning engineers, data scientists, and AI professionals. Get hands-on experience in important areas like data preprocessing, time series modeling, text mining, supervised and unsupervised learning.

The knowledge of machine language is preferred for many evolving job roles like data scientist, data analyst, machine learning engineer, AI engineer. With AI adoption being in the budding stages, the demand for machine learning professionals is on the rise. Companies across industries are looking for skilled machine learning professionals for their AI-powered projects.

After completing the free machine learning course, you will be awarded a Completion Certificate, which you can update on your resume or on social media platforms

2. Introduction to Artificial Intelligence Course

Learn the basics of AI with the free artificial intelligence basics program and other free machine learning courses that provide an overview of AI concepts and workflows, plus the basics of machine learning and deep learning. Gain AI knowledge alongside working on specific use cases. Learn the difference between concepts like supervised, unsupervised, and reinforcement learning. This course is the ideal launchpad for anyone looking to become an AI engineer.

AI is exponentially being used in every field from retail, shopping, IoT to sports, analytics, and manufacturing. There’s a staggering demand for AI professionals in every industry. If you’re looking to break into the market, there is a variety of career path options to consider after completing this course.

Once you’ve completed the free AI courses, you will be awarded a Completion Certificate, which you can update on your resume or on social media platforms.

3. Deep Learning for Beginners

SkillUp brings you the free introductory course to Deep Learning – one of the most in-demand skills in AI. This foundations program covers the fundamental concepts of deep learning, TensorFlow and its installation, various deep learning frameworks, convolutional neural networks, recurrent neural networks in Python, and Deep Learning applications.

The free Deep Learning course provides a pathway to break into the world of AI. Enroll now to gain the knowledge and skills to advance your career.

Get Completion Certificate after every course is completed, which you can update on your resume or on social media platforms.

4. Getting Started with Machine Learning Algorithms

Become an expert in Machine Learning and AI with our free course to learn Machine Learning algorithms. Gain in-depth knowledge on supervised learning algorithms and unsupervised learning algorithms, k-means clustering, PCA, reinforcement learning, and Q-learning. Discover how machine learning algorithms work and how you can apply them in data analysis and automation. By the end of the course, you’ll gain the skills required for a machine learning engineer.

Common career opportunities you can pursue after completing such free machine learning courses are Machine learning engineer, Data scientist, Artificial Intelligence engineer, NLP scientist, etc.

5. Introduction to Neural Network

Learn neural networks from scratch with free neural network training. The course covers the basics of neural networks and their different types. Topics covered include data processing by neurons, backpropagation, gradient descent algorithms, convolution neural networks, and recurrent neural networks.

Knowledge of neural networks is preferred for in-demand job roles like Neural network engineer, Data scientist, Data analyst, NLP scientist. Upon course completion, you will be awarded a Completion Certificate, which you can update on your resume or on social media platforms

6. TensorFlow for Beginners

This course is designed to make you understand TensorFlow – a popular end-to-end open-source framework for deep learning. This course will teach you the basics of TensorFlow, how to install it on Ubuntu, TensorFlow object detection API, and identification of objects in images and videos. Learn machine learning skills with TensorFlow to build and train powerful models.

Build TensorFlow skills to get job-ready for lucrative careers like Machine learning engineer, Data scientist, Business intelligence developer, NLP scientist. After completing free machine learning courses, you will be awarded a Completion Certificate, which you can update on your resume or on social media platforms.

7. Introduction to Machine Learning with R

The free course on Machine Learning with R teaches the basics of machine learning, its algorithms, like linear regression, logistic regression, decision tree, random forest, SVM, hierarchical clustering techniques, and its varied applications. The program also covers R programming in detail plus time series analysis in R.

There has been an exceptional surge in demand for skilled machine language engineers across industries worldwide. Learn machine language with R to make most of the career opportunities like ML Engineer, Analytics Manager, Business Analyst, Information Architect, Developer, etc.

8. Image Recognition Basics for Beginners Course

The free Course to Learn Image Recognition Basics helps learners focus on image processing and image recognition techniques for object detection. Get trained on how to process data from image files, classify types of images and work with various neural networks.

After completing the image learning certification, common career opportunities available are Machine learning engineer, Data scientist, Business intelligence developer, NLP scientist.

Once you’ve completed the course successfully, you will be awarded a Completion Certificate, which you can update on your resume or on social media platforms.

9. Introduction to Supervised and Unsupervised Machine Learning

The free course to learn Supervised and Unsupervised Machine Learning Basics will enable learners to explore different classification and regression techniques. Topics covered include decision trees and clustering methods. This course provides a comprehensive understanding of supervised and unsupervised learning.

If you want to step into the world of Machine Learning, accelerate your career with the Supervised & Unsupervised Machine Learning course by Simplilearn. Common careers after this certification are ML engineer, analytics manager, business analyst, and information architect.

Techniques in Machine Learning

Machine Learning techniques are divided mainly into the following 4 categories:

1. Supervised Learning

Supervised learning is applicable when a machine has sample data, i.e., input as well as output data with correct labels. Correct labels are used to check the correctness of the model using some labels and tags. Supervised learning technique helps us to predict future events with the help of past experience and labeled examples. Initially, it analyses the known training dataset, and later it introduces an inferred function that makes predictions about output values. Further, it also predicts errors during this entire learning process and also corrects those errors through algorithms.

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Example: Let’s assume we have a set of images tagged as ”dog”. A machine learning algorithm is trained with these dog images so it can easily distinguish whether an image is a dog or not.

2. Unsupervised Learning

In unsupervised learning, a machine is trained with some input samples or labels only, while output is not known. The training information is neither classified nor labeled; hence, a machine may not always provide correct output compared to supervised learning.

Although Unsupervised learning is less common in practical business settings, it helps in exploring the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

Example: Let’s assume a machine is trained with some set of documents having different categories (Type A, B, and C), and we have to organize them into appropriate groups. Because the machine is provided only with input samples or without output, so, it can organize these datasets into type A, type B, and type C categories, but it is not necessary whether it is organized correctly or not.

3. Reinforcement Learning

Reinforcement Learning is a feedback-based machine learning technique. In such type of learning, agents (computer programs) need to explore the environment, perform actions, and on the basis of their actions, they get rewards as feedback. For each good action, they get a positive reward, and for each bad action, they get a negative reward. The goal of a Reinforcement learning agent is to maximize the positive rewards. Since there is no labeled data, the agent is bound to learn by its experience only.

4. Semi-supervised Learning

Semi-supervised Learning is an intermediate technique of both supervised and unsupervised learning. It performs actions on datasets having few labels as well as unlabeled data. However, it generally contains unlabeled data. Hence, it also reduces the cost of the machine learning model as labels are costly, but for corporate purposes, it may have few labels. Further, it also increases the accuracy and performance of the machine learning model.

Sem-supervised learning helps data scientists to overcome the drawback of supervised and unsupervised learning. Speech analysis, web content classification, protein sequence classification, text documents classifiers., etc., are some important applications of Semi-supervised learning.

Applications of Machine Learning

Machine Learning is widely being used in approximately every sector, including healthcare, marketing, finance, infrastructure, automation, etc. There are some important real-world examples of machine learning, which are as follows:

Healthcare and Medical Diagnosis:

Machine Learning is used in healthcare industries that help in generating neural networks. These self-learning neural networks help specialists for providing quality treatment by analyzing external data on a patient’s condition, X-rays, CT scans, various tests, and screenings. Other than treatment, machine learning is also helpful for cases like automatic billing, clinical decision supports, and development of clinical care guidelines, etc.

Marketing:

Machine learning helps marketers to create various hypotheses, testing, evaluation, and analyze datasets. It helps us to quickly make predictions based on the concept of big data. It is also helpful for stock marketing as most of the trading is done through bots and based on calculations from machine learning algorithms. Various Deep Learning Neural network helps to build trading models such as Convolutional Neural Network, Recurrent Neural Network, Long-short term memory, etc.

Self-driving cars:

This is one of the most exciting applications of machine learning in today’s world. It plays a vital role in developing self-driving cars. Various automobile companies like Tesla, Tata, etc., are continuously working for the development of self-driving cars. It also becomes possible by the machine learning method (supervised learning), in which a machine is trained to detect people and objects while driving.

Speech Recognition:

Speech Recognition is one of the most popular applications of machine learning. Nowadays, almost every mobile application comes with a voice search facility. This ”Search By Voice” facility is also a part of speech recognition. In this method, voice instructions are converted into text, which is known as Speech to text” or “Computer speech recognition.

Google assistant, SIRI, Alexa, Cortana, etc., are some famous applications of speech recognition.

Traffic Prediction:

Machine Learning also helps us to find the shortest route to reach our destination by using Google Maps. It also helps us in predicting traffic conditions, whether it is cleared or congested, through the real-time location of the Google Maps app and sensor.

Image Recognition:

Image recognition is also an important application of machine learning for identifying objects, persons, places, etc. Face detection and auto friend tagging suggestion is the most famous application of image recognition used by Facebook, Instagram, etc. Whenever we upload photos with our Facebook friends, it automatically suggests their names through image recognition technology.

Product Recommendations:

Machine Learning is widely used in business industries for the marketing of various products. Almost all big and small companies like Amazon, Alibaba, Walmart, Netflix, etc., are using machine learning techniques for products recommendation to their users. Whenever we search for any products on their websites, we automatically get started with lots of advertisements for similar products. This is also possible by Machine Learning algorithms that learn users’ interests and, based on past data, suggest products to the user.

Automatic Translation:

Automatic language translation is also one of the most significant applications of machine learning that is based on sequence algorithms by translating text of one language into other desirable languages. Google GNMT (Google Neural Machine Translation) provides this feature, which is Neural Machine Learning. Further, you can also translate the selected text on images as well as complete documents through Google Lens.

Virtual Assistant:

A virtual personal assistant is also one of the most popular applications of machine learning. First, it records out voice and sends to cloud-based server then decode it with the help of machine learning algorithms. All big companies like Amazon, Google, etc., are using these features for playing music, calling someone, opening an app and searching data on the internet, etc.

Email Spam and Malware Filtering:

Machine Learning also helps us to filter various Emails received on our mailbox according to their category, such as important, normal, and spam. It is possible by ML algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier.

Commonly used Machine Learning Algorithms

Here is a list of a few commonly used Machine Learning Algorithms as follows:

Linear Regression

Linear Regression is one of the simplest and popular machine learning algorithms recommended by a data scientist. It is used for predictive analysis by making predictions for real variables such as experience, salary, cost, etc.

It is a statistical approach that represents the linear relationship between two or more variables, either dependent or independent, hence called Linear Regression. It shows the value of the dependent variable changes with respect to the independent variable, and the slope of this graph is called as Line of Regression.

Linear Regression can be expressed mathematically as follows:

y= a0+a1x+ ε

Y= Dependent Variable

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X= Independent Variable

a0= intercept of the line (Gives an additional degree of freedom)

a1 = Linear regression coefficient (scale factor to each input value).

ε = random error

The values for x and y variables are training datasets for Linear Regression model representation.

Types of Linear Regression:

  • Simple Linear Regression
  • Multiple Linear Regression

Applications of Linear Regression:

Linear Regression is helpful for evaluating the business trends and forecasts such as prediction of salary of a person based on their experience, prediction of crop production based on the amount of rainfall, etc.

Logistic Regression

Logistic Regression is a subset of the Supervised learning technique. It helps us to predict the output of categorical dependent variables using a given set of independent variables. However, it can be Binary (0 or 1) as well as Boolean (true/false), but instead of giving an exact value, it gives a probabilistic value between o or 1. It is much similar to Linear Regression, depending on its use in the machine learning model. As Linear regression is used for solving regression problems, similarly, Logistic regression is helpful for solving classification problems.

Logistic Regression can be expressed as an ‘S-shaped curve called sigmoid functions. It predicts two maximum values (0 or 1).

Mathematically, we can express Logistic regression as follows:

Types of Logistic Regression:

  • Binomial
  • Multinomial
  • Ordinal

K Nearest Neighbour (KNN)

It is also one of the simplest machine learning algorithms that come under supervised learning techniques. It is helpful for solving regression as well as classification problems. It assumes the similarity between the new data and available data and puts the new data into the category that is most similar to the available categories. It is also known as Lazy Learner Algorithms because it does not learn from the training set immediately; instead, it stores the dataset, and at the time of classification, it performs an action on the dataset. Let’s suppose we have a few sets of images of cats and dogs and want to identify whether a new image is of a cat or dog. Then KNN algorithm is the best way to identify the cat from available data sets because it works on similarity measures. Hence, the KNN model will compare the new image with available images and put the output in the cat’s category.

Let’s understand the KNN algorithm with the below screenshot, where we have to assign a new data point based on the similarity with available data points.

Applications of KNN algorithm in Machine Learning

Including Machine Learning, KNN algorithms are used in so many fields as follows:

  • Healthcare and Medical diagnosis
  • Credit score checking
  • Text Editing
  • Hotel Booking
  • Gaming
  • Natural Language Processing, etc.

K-Means Clustering

K-Means Clustering is a subset of unsupervised learning techniques. It helps us to solve clustering problems by means of grouping the unlabeled datasets into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.

Decision Tree

Decision Tree is also another type of Machine Learning technique that comes under Supervised Learning. Similar to KNN, the decision tree also helps us to solve classification as well as regression problems, but it is mostly preferred to solve classification problems. The name decision tree is because it consists of a tree-structured classifier in which attributes are represented by internal nodes, decision rules are represented by branches, and the outcome of the model is represented by each leaf of a tree. The tree starts from the decision node, also known as the root node, and ends with the leaf node.

Decision nodes help us to make any decision, whereas leaves are used to determine the output of those decisions.

A Decision Tree is a graphical representation for getting all the possible outcomes to a problem or decision depending on certain given conditions.

Random Forest

Random Forest is also one of the most preferred machine learning algorithms that come under the Supervised Learning technique. Similar to KNN and Decision Tree, It also allows us to solve classification as well as regression problems, but it is preferred whenever we have a requirement to solve a complex problem and to improve the performance of the model.

A random forest algorithm is based on the concept of ensemble learning, which is a process of combining multiple classifiers.

Random forest classifier is made from a combination of a number of decision trees as well as various subsets of the given dataset. This combination takes input as an average prediction from all trees and improves the accuracy of the model. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting. Further, It also takes less training time as compared to other algorithms.

Support Vector Machines (SVM)

It is also one of the most popular machine learning algorithms that come as a subset of the Supervised Learning technique in machine learning. The goal of the support vector machine algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. It is also used to solve classification as well as regression problems. It is used for Face detection, image classification, text categorization, etc.

Naïve Bayes

The naïve Bayes algorithm is one of the simplest and most effective machine learning algorithms that come under the supervised learning technique. It is based on the concept of the Bayes Theorem, used to solve classification-related problems. It helps to build fast machine learning models that can make quick predictions with greater accuracy and performance. It is mostly preferred for text classification having high-dimensional training datasets.

It is used as a probabilistic classifier which means it predicts on the basis of the probability of an object. Spam filtration, Sentimental analysis, and classifying articles are some important applications of the Naïve Bayes algorithm.

It is also based on the concept of Bayes Theorem, which is also known as Bayes’ Rule or Bayes’ law. Mathematically, Bayes Theorem can be expressed as follows:

Where,

  • P(A) is Prior Probability
  • P(B) is Marginal Probability
  • P(A|B) is Posterior probability
  • P(B|A) is Likelihood probability

Difference between machine learning and Artificial Intelligence

  • Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence, whereas Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences.
  • Artificial Intelligence is a technology used to create an intelligent system that enables a machine to simulate human behavior. Whereas, Machine Learning is a branch of AI which helps a machine to learn from experience without being explicitly programmed.
  • AI helps to make humans like intelligent computer systems to solve complex problems. Whereas, ML is used to gain accurate predictions from past data or experience.
  • AI can be divided into Weak AI, General AI, and Strong AI. Whereas, IML can be divided into Supervised learning, Unsupervised learning, and Reinforcement learning.
  • Each AI agent includes learning, reasoning, and self-correction. Each ML model includes learning and self-correction when introduced with new data.
  • AI deals with Structured, semi-structured, and unstructured data. ML deals with Structured and semi-structured data.
  • Applications of AI: Siri, customer support using catboats, Expert System, Online game playing, an intelligent humanoid robot, etc. Applications of ML: Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.
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Below is the list of Top 10 commonly used Machine Learning (ML) Algorithms:

  • Linear regression
  • Logistic regression
  • Decision tree
  • SVM algorithm
  • Naive Bayes algorithm
  • KNN algorithm
  • K-means
  • Random forest algorithm
  • Dimensionality reduction algorithms
  • Gradient boosting algorithm and AdaBoosting algorithm

How Learning These Vital Algorithms Can Enhance Your Skills in Machine Learning

If you’re a data scientist or a machine learning enthusiast, you can use these techniques to create functional Machine Learning projects.

There are three types of most popular Machine Learning algorithms, i.e – supervised learning, unsupervised learning, and reinforcement learning. All three techniques are used in this list of 10 common Machine Learning Algorithms:

1. Linear Regression

To understand the working functionality of Linear Regression, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arranging them using a combination of these visible parameters. This is what linear regression in machine learning is like.

In this process, a relationship is established between independent and dependent variables by fitting them to a line. This line is known as the regression line and is represented by a linear equation Y= a *X + b.

In this equation:

  • Y – Dependent Variable
  • a – Slope
  • X – Independent variable
  • b – Intercept

The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.

2. Logistic Regression

Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logit regression.

These methods listed below are often used to help improve logistic regression models:

  • include interaction terms
  • eliminate features
  • regularize techniques
  • use a non-linear model

3. Decision Tree

Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems. It works well in classifying both categorical and continuous dependent variables. This algorithm divides the population into two or more homogeneous sets based on the most significant attributes/ independent variables.

4. SVM (Support Vector Machine) Algorithm

SVM algorithm is a method of a classification algorithm in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.

5. Naive Bayes Algorithm

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.

A Naive Bayesian model is easy to build and useful for massive datasets. It’s simple and is known to outperform even highly sophisticated classification methods.

6. KNN (K- Nearest Neighbors) Algorithm

This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it’s more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.

KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!

Things to consider before selecting K Nearest Neighbours Algorithm:

  • KNN is computationally expensive
  • Variables should be normalized, or else higher range variables can bias the algorithm
  • Data still needs to be pre-processed.

7. K-Means

It is an unsupervised learning algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let’s call that number K) in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.

How K-means forms clusters:

  • The K-means algorithm picks k number of points, called centroids, for each cluster.
  • Each data point forms a cluster with the closest centroids, i.e., K clusters.
  • It now creates new centroids based on the existing cluster members.
  • With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.

8. Random Forest Algorithm

A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:

  • If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
  • If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during this process.
  • Each tree is grown to the most substantial extent possible. There is no pruning.

9. Dimensionality Reduction Algorithms

In today’s world, vast amounts of data are being stored and analyzed by corporates, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information – the challenge is to identify significant patterns and variables.

Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.

10. Gradient Boosting Algorithm and AdaBoosting Algorithm

Gradient Boosting Algorithm and AdaBoosting Algorithm are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.

In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.

Final Thoughts

Job roles and requirements are changing faster than ever, making skill gaps more evident across organizations and industries. It is predicted that today’s skills might fade into oblivion in the next 3 to 5 years. This is why professional survival and growth depend largely on your ability to stay up-to-date by gaining new and emerging skills. While these nine free courses are excellent places to start, there is a whole world of fantastic, future-ready courses available that you can explore for a fraction of what you’d have to pay for a traditional college degree. If you’re serious about giving your career a push in the right direction, they are worth seeking out.