40+ best online courses for AI, Machine Learning and Data Science

online courses for AI, machine learning and data science

If you are interested in learning about data science, artificial intelligence, machine learning, and deep learning, these courses will come in handy. Regrouping some of the best-selling courses available online, this selection covers it all, from theoretical intuition for beginners to manipulating data and programming algorithms.

The popular and best-rated courses listed below teach the most useful knowledge and best practices to understand AI and data science and develop business applications. With courses for all levels, from complete beginners looking to learn what data science and artificial intelligence are, to advanced programmers interested in learning new algorithms and deepening their expertise, you can be sure to find a course for you.

Carefully selected from some of the most popular online course platforms and delivered by some of the best teachers from top universities and schools, the hand-picked best-selling courses listed below are grouped by level. They can be followed entirely online, allowing you to learn on your own schedule, and allow you to pass exams and gain certificates to show your employers – or not pass them and just learn to practice AI and data science on your own.

Priced between 29 and 199 USD, these online courses will take you to the next level and allow you to become data scientists and professional AI developers at a much lower cost than traditional engineering education. Some “specializations” go beyond these prices as they regroup multiple courses delivered by a university or school with a final certificate. But they will allow you to gain complete training in a particular area of machine learning or data science. Attending one of the top specializations listed at the end of this article will also grant you a valuable certificate delivered by top universities and business schools to prove the acquired expertise.

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Courses for beginners in data science, artificial intelligence, and machine learning

• Artificial Intelligence

AI For Everyone (Biz strategy) – Andrew Ng, Deeplearning.ai

AI For Everyone (Biz strategy) - Andrew Ng

This non-technical course starts an overview of common AI terminology in order to develop towards applications for business. Already attended by more than 180,000 learners, it presents what AI can realistically do and how to spot opportunities to apply AI to problems in organizations. Focusing on business applications, it will give you an idea of what it feels like to build machine learning and data science projects, how to work with an AI team and build an AI strategy in a company and how to navigate ethical and societal discussions around AI.

Estimated completion time: 9 hours

Artificial Intelligence A-Z™: Learn How To Build An AI – Hadelin de Ponteves, Kirill Eremenko

Artificial Intelligence A-Z™ Learn How To Build An AI – Hadelin de Ponteves, Kirill EremenkoAlready followed by more than 100,000 people, this course intends to make students build an AI without previous coding experience using Python, merge AI with OpenAI Gym to learn as effectively as possible, and optimize the AI to reach its maximum potential. It will help beginners to gain expert AI skills with Python code templates, provide tutorials to understand the concepts of AI, real-world solutions and support from professional data scientists.

Video course duration: 16.5 hours

• Machine Learning

Machine Learning – Andrew Ng, Stanford University

Andrew Ng

This complete Stanford course will provide you a broad introduction to machine learning, data mining, and statistical pattern recognition. It presents the theory and the practical know-how, together with some of Silicon Valley’s best practices for innovation in machine learning and AI. Taught by Machine Learning guru and Stanford Professor, Andrew Ng, and already followed by more than 2.6 million students, this course’s topics include supervised learning, unsupervised learning and best practices in machine learning. It will also walk you through case studies and applications including smart robots, text understanding, computer vision, medical informatics, audio, and database mining.

Estimated completion time: 56 hours

Machine Learning A-Z™: Hands-On Python & R In Data Science – Kirill Eremenko, Hadelin de Ponteves

Machine Learning A-Z™: Hands-On Python & R In Data Science – Kirill Eremenko, Hadelin de PontevesDesigned by professional data scientists, this course aims at teaching complex theory, algorithms and coding libraries in a simple way. Complete with practical exercises based on real-life examples, it covers not only the theory but also provides hands-on practice to building models, together with Python and R code templates. Topics covered in this course already attended by more than 470,000 students include data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, dimensionality reduction, model selection and boosting.

Video course duration: 41 hours

Deep Learning

Structuring Machine Learning Projects – Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh, Deeplearning.ai

Attended by more than 170,000 learners, this course by Machine Learning experts including Andrew Ng, teaches how to build a successful machine learning project. Based upon numerous real-life ML project, it provides industry experience to help students diagnose errors in a machine learning system and prioritize the most promising directions for reducing error. It also shows how to appraise complex ML settings, how to apply end-to-end learning, transfer learning, and multi-task learning.

Note that this course is part of the Deep Learning Specialization (see below)

Estimated completion time: 7 hours

Deep Learning A-Z™: Hands-On Artificial Neural Networks – Kirill Eremenko, Hadelin de Ponteves

Deep Learning A-Z™: Hands-On Artificial Neural Networks - Kirill Eremenko, Hadelin de PontevesAlready attended by more than 180,000 people, this course provides a complete overview of deep learning. The tutorials are grouped into two volumes: supervised and unsupervised deep learning and they aim at providing an intuitive understanding of deep learning concepts. This course includes real-world business problems and code written step-by-step from scratch, which can be downloaded and applied to other projects. Furthermore, it also includes support from professional data scientists and an overview of how to use tools, including Tensorflow, Pytorch and many more.

Video course duration: 22.5 hours

• Data Science

Complete Data Science Bootcamp

Followed by more than 150,000 learners, this course will teach you the skills required to become a data scientist, including big data, business intelligence, business analytics, machine learning, and artificial intelligence. To do so it covers the necessary calculus and linear algebra needed, statistics and programming with Python. Furthermore, it also dives into the Tableau software for visualization, advanced statistics for modeling, machine learning techniques and deep learning methods with TensorFlow.

Video course duration: 27 hours

SQL for Data Science – Sadie St. Lawrence, University of California, Davis

SQL for Data Science - Sadie St. Lawrence, University of California, Davis

This course, already by more than 110,000 students, covers the fundamentals of SQL and working with data to analyze it for data science purposes. Starting from the basics, it builds up to gradually create more complex queries for different types of data, filter and pare down results, allowing to interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape data for targeted analysis purposes.

Estimated completion time: 20 hours

Data Science A-Z™: Real-Life Data Science Exercises Included – Kirill Eremenko

Data Science A-Z™: Real-Life Data Science Exercises Included - Kirill EremenkoThis course provides a full overview and hands-on techniques for data science. Already attended by more than 110,000 learners, it will teach you how to clean and prepare data for analysis, how to perform basic visualization, how to model and curve-fit data, and how to present findings and wow the audience. It will also give you practical exercises and a good understanding of data science tools including, SQL, SSIS, Tableau, and Gretl.

Video course duration: 21 hours

Python for Data Science and AI – Joseph Santarcangelo, IBM

This beginner-friendly course is an introduction to Python for data science and programming. Already attended by more than 80,000 people, it covers the basics of Python basics, data structures, programming fundamentals and working with data in Python. Finally, this course will go through a programming project to test the newly acquired skills.

Estimated completion time: 12 hours

IBM Data Science Professional Certificate – Joseph Santarcangelo, Rav Ahuja, Polong Lin, Alex Aklson, Saeed Aghabozorgi, IBM

IBM Data Science Professional CertificateAlready attended by more than 80,000 students, this course covers a wide array of data science topics including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. It concludes with a hands-on assignment in the IBM Cloud using real data science tools and real-world data sets. Upon completion of this course, you will receive a  digital Badge from IBM recognizing your proficiency in data science.

Estimated completion time: 3 months

Advanced courses for data science, machine learning, deep learning, and specific topics

• Machine Learning

Neural Networks and Deep Learning – Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh, Deeplearning.ai

Already attended by more than 450,000 students, this course covers the foundations of deep learning. Taught by Machine Learning experts including Andrew Ng, it presents the major technology trends driving deep learning, how to build, train and apply fully connected deep neural networks, how to implement efficient neural networks and the key parameters in a neural network’s architecture. It dives deep into how deep learning works, allowing students to apply deep learning in their own applications.

Note that this course is part of the Deep Learning Specialization (see below)

Estimated completion time: 18 hours

Programming code

Machine Learning with Python – Saeed Aghabozorgi, IBM

After a presentation of machine learning and its applications, the course gives an overview of machine learning topics such as supervised vs unsupervised learning, model evaluation, and machine learning algorithms using the Python programming language. Complete with real-life examples practice, this course covers regression, classification, clustering, and includes projects such as cancer detection, predicting economic trends or customer churn, and recommendation engines. An IBM digital badge will be awarded to students upon the successful completion of this course.

Estimated completion time: 17 hours

• Deep Learning

Data Science: Deep Learning in Python

Data Science: Deep Learning in PythonThis course aims at providing you a hands-on approach to building machine learning models using Python, Numpy and TensorFlow. It goes beyond basic models like logistic and linear regression to automatically learn features. Based upon practical examples, this course will teach you how to make user behavior predictions, facial expression recognition, together with the newest developments in neural networks and what they are used for.

Video course duration: 10.5 hours

• Data Science

Introduction to Data Science in Python – Christopher Brooks, University of Michigan

University of Michigan logoThis course introduces the basics of the python programming environment, data manipulation and cleaning techniques. Thanks to this course already attended by more than 290,000 people, you will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. It should be taken as an introduction to other courses on applied data science with Python.

Estimated completion time: 18 hours

Python for Data Science and Machine Learning Bootcamp – Jose Portilla

Python for Data Science and Machine Learning Bootcamp - Jose PortillaThis comprehensive course dives into how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms. Designed for both beginners with some programming experience or experienced developers, and already attended by more than 250,000 students, this course notably covers programming with Python, Numpy and pandas libraries, web scraping, connecting to SQL, Matplotlib, seaborn and Plotly for data visualizations, as well as machine learning algorithms, natural language processing, neural nets and deep learning

Video course duration: 22.5 hours

Tableau 10 A-Z: Hands-On Tableau Training For Data Science – Kirill Eremenko

This course teaches data visualization through Tableau 10 to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. Already attended by more than 100,000 students, it will teach you how to navigate the software, connect Tableau to a variety of datasets, analyze, blend, join and calculate data, and visualize data in the form of various charts, plots, and maps.

Video course duration: 7.5 hours

R Programming A-Z™: R For Data Science With Real Exercises – Kirill Eremenko

This course is a step-by-step lesson, already followed by more than 100,000 learners to learn R programming by doing. It presents valuable concepts through live examples that can be applied right away, each time allowing to move one extra step forward. Packed with real-life analytical challenges to solve directly or as homework exercises, this course allows anyone, no matter his previous knowledge to successfully master R programming.

Video course duration: 10.5 hours

Machine Learning, Data Science and Deep Learning with Python – Frank Kane

Machine Learning, Data Science and Deep Learning with Python - Frank KaneDesigned for students with previous coding experience, this course covers the techniques used by real data scientists and machine learning and includes hands-on Python code examples and a final project to apply the acquired skills. It dives into machine learning, AI, and data mining techniques including deep learning, neural networks with TensorFlow and Keras, data visualization in Python with MatPlotLib and Seaborn, AI algorithms, applications and machine learning with Apache Spark.

Video course duration: 14 hours

• Reinforcement Learning

Artificial Intelligence: Reinforcement Learning in Python

Artificial Intelligence: Reinforcement Learning in PythonThis course focuses on Reinforcement Learning, one of the most recent and most powerful techniques for Machine Learning, which led to new and amazing insights both in behavioral psychology and neuroscience. Attended by more than 25,000 students, it notably covers the multi-armed bandit problem and the explore-exploit dilemma, ways to calculate means and moving averages and their relationship to stochastic gradient descent, Markov Decision Processes, Dynamic Programming, Monte Carlo and how to apply Q-Learning to build a stock trading bot.

Video course duration: 9.5 hours

• Computer Vision

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs – Hadelin de Ponteves, Kirill Eremenko

This course focuses on computer vision, how the most popular computer vision methods work and how to apply them in practice. Followed by more than 25,000 students, it dives into this branch of AI where there is the most to create, from health to retail to entertainment. This course covers the best codes, libraries, and tools, guiding learners through the field to maximize the advantage of using Computer Vision.

Video course duration: 11 hours

• Natural Language Processing

Natural Language Processing with Deep Learning in Python

Natural Language Processing with Deep Learning in PythonThis course, already followed by more than 25,000 people, focuses on advanced Natural Language Processing. It will teach you some of the most important algorithms and techniques for NLP, including word2vec which maps words to a vector space, GloVe which uses matrix factorization, parts-of-speech tagging, named entity recognition, recurrent neural networks, and recursive neural networks. Providing a range of materials, the course primarily relies on the Numpy, Matplotlib, and Theano libraries for Python.

Video course duration: 13 hours

• TensorFlow

TensorFlow logo

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning – Laurence Moroney, Google Brain, Deeplearning.ai

Designed for software developers interested in scalable AI-powered algorithms, this course teaches the best practices for using TensorFlow, one of the most popular open-source framework for machine learning. Already attended by more than 90,000 people, this course given by Laurence Moroney of Google AI details the foundation principles of Machine Learning and Deep Learning and how to use TensorFlow to implement those principles to build and apply scalable models to real-world problems.

Estimated completion time: 9 hours

Complete Guide to TensorFlow for Deep Learning with Python – Jose Portilla

Already followed by more than 60,000 learners, this course aims to give an understanding of Google’s TensorFlow framework and a guide to using the TensorFlow framework with the latest techniques available in deep learning. Designed to balance theory and practical implementation, it will provide you the codes with complete Jupyter notebooks, slides, and notes on topics including neural networks, Densely Connected Networks, Convolutional Neural Networks, Recurrent Neural Networks, AutoEncoders, Reinforcement Learning, and OpenAI Gym.

Video course duration: 14 hours

Robot playing the piano

Specializations: series of advanced courses for data science, machine learning, and deep learning

Delivered by top universities and schools, these series of courses provide a complete view of one particular aspect of artificial intelligence, data science, machine learning, etc. Expecting months of study to be completed, these specializations will teach you thorough AI and data science knowledge to suit your purposes and goals. Though they will require time and assiduity, completing them will be rewarded with valuable and widely recognized certifications that will help you demonstrate your newly acquired expertise to potential employers.

Note that these courses can obviously also be attended separately as needed, and to make your own curriculum to learn the specific skills that will help you achieve your purpose at hand or fit your interests.

Data Science Specialization – Johns Hopkins University

Johns Hopkins University logoThis Specialization already followed by more than 260,000 students, details the concepts and tools needed to ask the right kinds of questions to make inferences and publishing results throughout the entire data science pipeline. It will finish with a Capstone Project which will make you apply the skills learned by building a data product using real-world data. When they will complete the course, students will be equipped with a portfolio demonstrating their mastery of the material.

Estimated completion time: 8 months

Deep Learning Specialization – deeplearning.ai

deeplearning.ai logoThis Specialization will help you become good at Deep Learning. In five courses, you will learn its foundations, understand how to build neural networks, and learn how to lead successful machine learning projects. Already followed by more than 250,000 people, it will notably cover Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and includes case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. With both theory and applications, the course will teach and be put into practice with Python and TensorFlow.

Estimated completion time: 3 months

Machine Learning Specialization – University of Washington

University of Washington logoThis Specialization will cover a series of practical case studies that will help gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. It will teach you to analyze large and complex datasets, create systems that can adapt and improve over time, and build intelligent applications to make predictions from data.

Estimated completion time: 8 months

Here is our selection of the best online courses for AI, data science and machine learning. Did we forget any? Did you complete one of these courses? How did you benefit from it? Let us know in the comments below! 

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