15+ Best AI Сourses to Learn in 2023: Free and Paid

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by Damir Yalalov

Published: March 02, 2023 at 12:56 am Updated: March 02, 2023 at 12:56 am

To improve your local-language experience, sometimes we employ an auto-translation plugin. Please note auto-translation may not be accurate, so read original article for precise information.

Artificial intelligence is a rapidly growing field that has the potential to revolutionize the way we live and work. From self-driving cars to personalized healthcare, AI has already made a significant impact on many industries and continues to advance at an unprecedented pace.

As more and more companies and organizations incorporate AI into their operations, the demand for skilled professionals in this field is rapidly increasing.

To meet this demand, there are now a wide range of AI courses available, both free and paid, online and in-person. These courses cover a variety of topics, from the basics of machine learning and deep learning to more specialized areas such as natural language processing and computer vision. They are designed for students, professionals, and anyone interested in learning about this exciting field.

The list contains the best AI courses currently available, including courses from top universities like Stanford and MIT and industry leaders like Google and IBM. It caters to both beginners new to machine learning and experienced professionals seeking to expand their knowledge.

Best AI Сourses Comparison Sheet

There are numerous AI courses available both online and offline, free and paid, from various reputable institutions across the world. Here are some of the best AI courses:

Research and compare AI courses to find the best fit for your learning needs and goals. Keep in mind that AI is a rapidly evolving field, so staying up-to-date with the latest developments and advancements is crucial.

The study of AI is also important in designing the programs of our future: Top 120+ AI Generated Content in 2023: Images, Music, Videos

Best Free AI Courses

AI for Everyone

The “AI for Everyone” course on Coursera is an introductory course that provides a comprehensive overview of the field of artificial intelligence (AI). The course is designed for individuals who are interested in learning about AI but do not necessarily have a technical background in the field.

The course is taught by Andrew Ng, a leading AI researcher and co-founder of Coursera. It consists of four weeks of material, each containing several video lectures and quizzes. The course covers a wide range of topics related to AI, including machine learning, neural networks, computer vision, natural language processing, and robotics.

The course covers fundamental principles of AI, including machine learning algorithms and techniques and their practical applications. Learners will explore ethical and social considerations associated with AI. The course covers AI utilization in diverse sectors such as healthcare, finance, and transportation.

Overall, the “AI for Everyone” course is a great introduction to the field of AI, and is suitable for anyone who wants to gain a foundational understanding of the subject. It does not require any prior technical knowledge, and can be completed at your own pace.

Supervised Machine Learning: Regression and Classification

The “Supervised Machine Learning: Regression and Classification” course on Coursera is a popular online course taught by Andrew Ng, a leading AI researcher and co-founder of Coursera. This course is designed to provide a comprehensive introduction to machine learning, which is a subfield of artificial intelligence that focuses on the development of algorithms that can learn from data.

The course consists of 11 weeks of material, each containing several video lectures, quizzes, and programming assignments. However, the course covers a wide range of topics related to machine learning, including linear regression, logistic regression, neural networks, support vector machines, clustering, and anomaly detection.

Overall, the “Machine Learning” course on Coursera is an excellent resource for anyone who wants to gain a solid foundation in machine learning. One of the most respected experts in the field teaches the course and provides a comprehensive overview of the subject.

Deep Learning

The “Deep Learning” specialization on Coursera is a comprehensive online course taught by Andrew Ng and a team of expert instructors. The design of this specialization is to offer a thorough introduction to deep learning. It is a subfield of machine learning that concentrates on teaching artificial neural networks with multiple layers to enhance the accuracy of predictions and classifications.

The specialization consists of five courses, each covering different aspects of deep learning. The courses are:

1. Neural Networks and Deep Learning: This course covers the basics of deep learning and neural networks, including how to build and train them.
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization: This course covers advanced techniques for improving the performance of deep neural networks, including hyperparameter tuning, regularization, and optimization.
3. Structuring Machine Learning Projects: This course teaches you how to structure machine learning projects, including how to diagnose and fix errors in your models.
4. Convolutional Neural Networks: This course covers convolutional neural networks, which are commonly used in computer vision tasks such as image recognition.
5. Sequence Models: This course covers sequence models, which are used in natural language processing and other applications involving sequential data.

Overall, thousands of students worldwide have completed the highly regarded “Deep Learning” specialization on Coursera. The course targets individuals with a fundamental knowledge of programming and machine learning and aims to teach them the latest techniques in deep learning.

Applied Artificial Intelligence from IBM

The “Applied Artificial Intelligence from IBM” professional certificate on Coursera is a program offered by IBM Watson AI. This certificate is designed to provide learners with the skills and knowledge needed to build and deploy AI solutions in real-world settings.

The program consists of six courses, each covering different aspects of applied artificial intelligence. The courses are:

1. Introduction to Artificial Intelligence: This course provides an overview of AI, including its history, basic principles, and applications.
2. Getting Started with AI using IBM Watson: This course teaches you how to use IBM Watson to build and deploy AI solutions.
3. Building AI Applications with Watson APIs: This course covers how to use various Watson APIs to build and deploy AI applications.
4. Building Chatbots with Watson APIs: This course teaches you how to use Watson APIs to build chatbots for customer service and other applications.
5. Introduction to Computer Vision with Watson and OpenCV: This course covers computer vision and how to use Watson and OpenCV to build computer vision applications.
6. Building AI Applications with TensorFlow: This course covers TensorFlow, which is a popular framework for building and deploying deep learning models.

To summarize, throughout the program, you will learn about the practical aspects of building and deploying AI solutions, including how to preprocess data, train models, and evaluate performance. You will also learn about the ethical and social implications of AI, including bias and fairness.

CS50’s Introduction to Artificial Intelligence with Python

“CS50’s Introduction to Artificial Intelligence with Python” is an online course offered by Harvard University through edX. This course is designed to provide an introduction to artificial intelligence and machine learning using the Python programming language.

The course consists of several modules, each containing video lectures, quizzes, and programming assignments. The course covers a range of topics related to artificial intelligence and machine learning, including search algorithms, optimization, machine learning, and deep learning.

To summarize, “Introduction to AI with Python” is a highly regarded course that provides a solid foundation in artificial intelligence and machine learning. However, the course has been completed by thousands of learners worldwide. It is suitable for anyone interested in learning about these topics, and it is designed for individuals with some programming experience. Prior knowledge of AI or machine learning is not required. So, upon completion of the course, learners will have the skills and knowledge to apply AI and machine learning techniques to real-world problems.

Data and AI Fundamentals

“Data and AI Fundamentals” is an online course offered by Microsoft through edX. This course is designed to provide an introduction to data analysis and artificial intelligence (AI).

The course consists of several modules, each containing video lectures, quizzes, and hands-on labs. The course covers a range of topics related to data analysis and AI, including data types and sources, data wrangling, data visualization, machine learning, and deep learning.

Throughout the course, you will learn how to use various tools and platforms, including Azure Machine Learning, Python, and Jupyter Notebooks, to perform data analysis and build AI models. You will also learn about the ethical and social implications of AI, including fairness, privacy, and security.

Overall, Individuals with some programming experience can take the course, and they do not need any prior knowledge of data analysis or AI. It is a self-paced course, and learners can take as much time as they need to complete the course material.

Introduction to Machine Learning

“Introduction to Machine Learning” is an online course offered by Udacity that provides an introduction to the fundamentals of machine learning. The course targets individuals who possess some programming experience, but not necessarily any previous exposure to machine learning.

Each lesson of the course covers a different aspect of machine learning. These include supervised and unsupervised learning, feature scaling, cross-validation, overfitting, and performance metrics. Moreover, the course uses the Python programming language and the scikit-learn library to implement and apply the machine learning algorithms.

To summarize, the course allows learners to complete it at their own pace, without any time restrictions. The course includes video lectures, quizzes, and programming assignments to provide hands-on experience with machine learning algorithms. The course is designed to help learners improve their understanding of machine learning concepts and techniques.

Reinforcement Learning

The “Reinforcement Learning Course by David Silver” is a series of video lectures on Reinforcement Learning (RL) that was first offered in 2015 by David Silver, a researcher at DeepMind. The course consists of 10 video lectures, each lasting approximately 1-2 hours, and covers a wide range of topics related to RL, including Markov Decision Processes, Monte Carlo methods, Temporal Difference learning, and deep reinforcement learning.

The course is suitable for individuals with a background in mathematics, computer science, or related fields. It provides a comprehensive introduction to RL, including both theory and practical examples.
Thousands of learners worldwide have viewed the lectures. The course is a popular resource for students and researchers interested in RL.

As an AI language model, I cannot provide real-time updates on the course’s current state in 2023. However, given its popularity and usefulness, it is likely that the material is still relevant and valuable for anyone interested in learning about RL.

TensorFlow Developer Professional Certificate

The “TensorFlow Developer” Professional Certificate is an online program offered by Coursera in collaboration with deeplearning.ai. The program aims to instruct learners on building and deploying deep learning models using TensorFlow, an open-source software library created by Google.

The program consists of four courses, each of which covers a different aspect of deep learning using TensorFlow. The courses are:

1. Introduction to TensorFlow for AI, Machine Learning, and Deep Learning: This course provides an introduction to TensorFlow and covers the basics of building and training deep learning models.
2. Convolutional Neural Networks in TensorFlow: This course focuses on convolutional neural networks (CNNs), a type of neural network commonly used for image classification, and teaches learners how to build and train CNNs using TensorFlow.
3. Natural Language Processing in TensorFlow: This course covers natural language processing (NLP) techniques, such as text classification and sentiment analysis, and teaches learners how to apply these techniques using TensorFlow.
4. Sequences, Time Series and Prediction: This course teaches learners how to build and train recurrent neural networks (RNNs) and other deep learning models to analyze time series data.

The program is self-paced, and learners can take as much time as they need to complete each course. Each course includes video lectures, quizzes, and programming assignments, which learners must complete to earn a certificate.

Practical Deep Learning for Coders

The fast.ai course is an online course on deep learning and machine learning offered by fast.ai. Fast.ai is a research lab and educational organization founded by Jeremy Howard and Rachel Thomas. The course aims to be pragmatic and experiential. So, the course educates learners on how to fabricate deep learning models utilizing Python and the fastai library.

The course consists of two parts: the “Practical Deep Learning for Coders” course and the “Cutting Edge Deep Learning for Coders” course. The first part of the course covers the basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. So, the second part of the course covers more advanced topics in deep learning, including generative models, reinforcement learning, and natural language processing.

The course aims to be inclusive to learners of all proficiency levels and does not necessitate any prior knowledge of machine learning or deep learning. Moreover, the course employs Jupyter notebooks for instruction and involves practical coding exercises that learners can carry out using Google Colaboratory.

Some of the key topics covered in the course include:

• Image classification
• Object detection
• Natural language processing
• Recommendation systems
• Generative models
• Reinforcement learning

So, learners who complete the course will understand deep learning and machine learning concepts and have the skills to build and deploy deep learning models for various applications. The course is respected in the field of machine learning, and experts recommend it as a starting point for beginners.

Machine Learning Crash Course

The Google Machine Learning Crash Course is a free online course offered by Google that provides an introduction to machine learning concepts, tools, and techniques. The course targets developers with minimal or no experience in machine learning, and its aim is to offer a fast and pragmatic overview of the field.

So, the course is segmented into numerous modules, each covering a distinct aspect of machine learning. These modules include:

1. Introduction to Machine Learning. This module provides an overview of the basic concepts and terminology used in machine learning, and introduces learners to supervised learning, unsupervised learning, and reinforcement learning.
2. Machine Learning with TensorFlow. This module provides an introduction to the TensorFlow framework, which is used by Google to develop machine learning models.
3. Generalization, Overfitting, and Underfitting. This module explains the concepts of generalization, overfitting, and underfitting, and how to avoid them when building machine learning models.
4. Neural Networks. This module provides an introduction to neural networks, which are a class of machine learning models that are inspired by the structure of the brain.
5. Training Neural Networks.This module explains how to train neural networks using backpropagation, and introduces techniques for improving the performance of neural networks.
6. Deep Neural Networks: This module provides an introduction to deep neural networks, which are neural networks with multiple layers.
7. TensorFlow Programming: This module provides an introduction to TensorFlow programming, and covers topics such as tensors, operations, and graphs.

To summarize, the course comprises video lectures, interactive exercises, and programming assignments, and learners can finish it at their own pace. Upon completion of the course, learners will have a basic understanding of machine learning concepts and techniques, and will be able to use TensorFlow to build simple machine learning models.

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Best Paid AI Courses

AI Programming with Python

The “AI Programming with Python” Nanodegree program offered by Udacity is designed to provide learners with a comprehensive introduction to artificial intelligence and machine learning using the Python programming language.

The program consists of five courses, each covering different aspects of AI and machine learning. The courses are:

1. Introduction to Python Programming. This course covers the basics of Python programming, including data structures, control structures, and functions.
2. Introduction to Machine Learning with Python. This course teaches you how to build and evaluate machine learning models using popular libraries such as NumPy, Pandas, and Scikit-learn.
3. Deep Learning with PyTorch. This course covers deep learning, including how to build and train neural networks using the PyTorch library.
4. Applied AI: This course covers various applications of AI, including natural language processing, computer vision, and game playing.
5. AI Capstone Project. In this course, you will apply the knowledge and skills you have learned in the previous courses to a real-world project.

Throughout the program, you will learn how to preprocess data, train models, and evaluate performance using Python and various libraries. You will also learn about the ethical and social implications of AI, including bias and fairness.

The “AI Programming with Python” Nanodegree program targets individuals with some programming experience, but it does not demand any prior knowledge of AI or machine learning. It is a self-paced program, and learners can take as much time as they need to complete the course material.

To summarize, the program is highly regarded and has been completed by thousands of learners worldwide. Upon completion of the program, learners will have the skills and knowledge needed to apply artificial intelligence and machine learning techniques to real-world problems. They will also have a portfolio of projects to showcase their skills to potential employers.

Artificial Intelligence A-Z: Learn How To Build An AI

“Artificial Intelligence A-Z: Learn How To Build An AI” is an online course offered by Udemy that provides a comprehensive introduction to artificial intelligence (AI) and machine learning. The course is designed for individuals with no prior knowledge of AI or programming.

The course covers various topics related to AI and machine learning. Topics include supervised and unsupervised learning, deep learning, natural language processing, and computer vision. The course also provides practical training on how to use various tools and platforms, including Python, TensorFlow, and Keras.

The course includes over 40 hours of video lectures. It includes quizzes and coding exercises. The quizzes and coding exercises enable learners to practice their skills. So, learners gain hands-on experience with AI and machine learning algorithms through these quizzes and coding exercises. The course also includes several projects that allow learners to apply their knowledge to real-world problems.

Since the course is self-paced, learners can take as much time as they require to complete the material. Additionally, the course is appropriate for anyone interested in acquiring knowledge about AI and machine learning, regardless of their background or experience level.

Upon completion of the course, learners will have a solid understanding of how to use AI and machine learning to solve problems. The course also provides a foundation for further study and more advanced courses in AI and machine learning.

Neural Networks and Deep Learning

The “Neural Networks and Deep Learning” course is an online course offered by Coursera and taught by Andrew Ng, a professor at Stanford University and a co-founder of Google Brain. The course provides an introduction to deep learning, a subfield of machine learning that uses artificial neural networks to model complex patterns and relationships in data.

The course targets individuals who possess a fundamental comprehension of Python programming and linear algebra. It covers a range of topics related to neural networks and deep learning, including convolutional neural networks, recurrent neural networks, and deep learning frameworks such as TensorFlow and Keras. The course also includes practical coding assignments that enable learners to practice their skills and implement various deep learning algorithms.

The course consists of four modules, each of which includes video lectures, quizzes, and programming assignments.

Since the course is self-paced, learners can take as much time as they need to complete it.

Upon completion of the course, learners will have a solid understanding of the principles of deep learning, including the ability to build and train neural networks for various applications.

To summarize, the “Neural Networks and Deep Learning” course is a well-known and popular learning resource among individuals interested in deep learning, and thousands of learners worldwide have completed it.

Data Science and Machine Learning Bootcamp with R

The “Data Science and Machine Learning Bootcamp with R” is an online course offered by Udemy. This course intends to instruct learners on the basics of data science and machine learning using the R programming language.

The course targets beginners and does not demand any prior knowledge of programming or data science. The course covers a wide range of topics, including data manipulation, data visualization, statistical inference, machine learning algorithms, and model evaluation.

So, Data Science and Machine Learning course comprises 19 sections, and it offers over 100 lectures, quizzes, and programming assignments. Each section covers a specific topic and includes video lectures, code examples, and exercises that help learners practice their skills.

Some of the key topics covered in the course include:

• Data wrangling and manipulation using dplyr and tidyr
• Data visualization using ggplot2
• Probability and statistical inference
• Linear regression and multiple regression
• Classification and regression trees
• Random forests and gradient boosting
• Clustering and dimensionality reduction
• Time series analysis

So, upon completion of the course, learners will have a solid understanding of the R programming language and its applications in data science and machine learning. They will also have the skills to analyze and interpret complex data sets, build and evaluate predictive models, and communicate their findings effectively to others.

Conclusion

In conclusion, learning about AI is becoming increasingly important in today’s digital age, as AI is rapidly transforming various industries and changing the way we live and work. By studying AI, individuals can develop the knowledge and skills necessary to design and develop intelligent systems that can learn from data and make predictions or decisions.

Many fields, including healthcare, finance, transportation, and education, are using AI, and experts predict that its applications will continue to grow in the coming years.

There are many online courses and resources available for learning about AI, ranging from introductory courses to more advanced ones that cover topics such as deep learning and reinforcement learning. By investing in AI education, individuals can stay up-to-date with the latest developments in the field, acquire valuable skills that are in high demand, and potentially open up new career opportunities.

Overall, to remain competitive in today’s job market and be ready for the future of work, individuals must learn AI as it will have an increasingly significant impact on various aspects of our lives.

FAQ

AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, problem-solving, and decision-making.

There are three main types of AI: narrow or weak AI, general AI, and superintelligence. Narrow AI is designed to perform a specific task, while general AI is capable of performing any intellectual task that a human can. Superintelligence, which is still purely theoretical, refers to AI that surpasses human intelligence and is capable of solving problems beyond our comprehension.

AI has many practical applications across various industries, such as healthcare, finance, transportation, and education. Examples include predictive maintenance in manufacturing, personalized medicine in healthcare, fraud detection in finance, and intelligent traffic management in transportation.

To work in AI, one needs a strong foundation in math, statistics, and programming, as well as knowledge of machine learning algorithms and frameworks such as TensorFlow, Keras, and PyTorch.

There are many online resources available for learning about AI, including free online courses, tutorials, and MOOCs offered by top universities and companies such as Google, Coursera, Udacity, and edX.

AI raises many ethical concerns, such as bias, privacy, and job displacement. It is important for individuals and organizations to consider these issues when developing and deploying AI systems.

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About The Author

Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet. 

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Damir Yalalov

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Damir Yalalov

Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet. 

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