Looking to learn Artificial Intelligence skills? One of these AI courses, certifications or training programs will help you gain proficiency and prepare you for a promising career in artificial intelligence and machine learning.
Artificial Intelligence (AI) is the skill of the future. It has been estimated that by 2030, AI market will contribute more than $15 trillion to the world economy. There is a huge skill shortage in the field of artificial intelligence therefore if you are entering the workforce, getting skilled in AI can guarantee a promising future-proof career. For those already in the workforce, re-skilling and up-skilling with future oriented job skills like AI is more relevant now than ever.
We are seeing artificial intelligence in a myriad of applications across industries. Be it healthcare, finance, mobile, automobile, smart home devices, music and movie recommendation services, retail, security surveillance, fraud detection, virtual player games, social media apps, the possibilities are endless. Almost every business is trying to implement AI in their processes and products. Learning AI can therefore open a world of opportunities for anyone. A combination of Artificial Intelligence, Machine learning and deep learning can chart out a way to great career prospects.
Learning Artificial Intelligence (though not very easy) has become very accessible now with a variety of courses and trainings available online. These are taught by best AI educators, researchers and experts, and often come at a cost much less than a typical college course. Some of these classes are very comprehensive and include curriculum of an equivalent college degree. Some of these are even available for free and are perfect to get a glimpse into the world of AI.
To help you make the right choice, we’ve compiled this list of best artificial intelligence courses, classes, certifications, training programs and tutorials available online that you can use to gain a good grounding in the field of AI.
This Stanford Machine Learning Course has been created by Andrew Ng, the most renowned expert in AI and Machine Learning, cofounder of Coursera, founding lead of Google’s deep learning research unit Google Brain, former head of AI at Baidu, and currently CEO at Landing AI. The popularity of this ML course can be gauged from the fact that around 3.5 million students and professionals have already taken this course and 93% of them have given it a 5-star rating. Undoubtedly, AI experts often cite this course as the single most important resource for anyone looking to learn AI and ML.
This course introduces learners to the core ideas of machine learning, datamining and statistical pattern recognition. It imparts them a good grounding in the mathematical, statistical, and computer science fundamentals that form the basis of automated learning machines. The course material is very extensive with around 55 hours of content spread over 11 weeks. It covers the following topics:
- Review of linear algebra
- Linear Regression with one and multiple variables
- Logistic Regression and its application to multi-class classification
- Supervised Learning
- Regularization to prevent ML models from overfitting the training data
- Support Vector Machines
- Neural Networks
- Backpropagation algorithm for neural networks
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender algorithms
- Deep Learning
- Applying machine learning algorithms with large datasets
- Performance of a machine learning system with multiple parts
- Best practices for applying machine learning
For programming assignments, the course uses the open-source programming language Octave, which is a simple way to learn the fundamentals of ML. There is a tutorial included for Octave/MATLAB in the course.
Numerous case studies and applications are included in the course to help learners get hands-on practice. They get to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
- Highest rated amongst the top free Machine Learning and AI courses available online
- Excellent fit for beginners in the field of artificial intelligence and machine learning
- Learn about the most effective machine learning techniques, and gain practice implementing them
- Learn about some of Silicon Valley’s best practices in the field of Machine Learning and AI innovation
- Gain the practical know-how needed to quickly and powerfully apply ML techniques to new real life situations and problems
- Study the course for free; option to get a paid certificate for showcasing your learning of AI and ML skills
Duration : 11 weeks, 5-6 hours per week
Rating : 4.9
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The part of AI that is rising rapidly and driving a lot of developments and transformations that AI is touted for is Deep Learning. This Coursera Deep Learning specialization created and taught by Andrew Ng is a more advanced course series for those looking to learn about AI and deep learning, how to apply it to solve problems and build a career in AI. Since it is not an entry level program, learners are expected to have Python programming and mathematics skills and some knowledge and experience in machine learning. This specialization is in fact cited as the next logical follow up to Andrew Ng’s Machine Learning course on Coursera.
This is a five course specialization where students learn the important technical skills and tools of deep learning. These courses cover the following topics:
- Foundations of Neural networks
- How to build deep neural networks and train them on data
- Practical aspects of deep learning, like hyperparameter tuning, regularizations and optimization
- Structure machine learning projects
- How to set up train/dev/test sets
- End-to-End deep learning and when you should use it
- Build Convolutional neural networks and apply to image data
- Sequence models and how to apply them to natural language processing problems
Alongside this, courses cover various real-world case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. The students get to work on many exciting projects from building a face recognition system, to building a simple translation system, and more. Furthermore, there are interviews and discussions with top leaders and pioneers in the field that give students career advice, inspiration and help them comprehend situations that they are likely to face in the real world.
- Master the theory of AI and deep learning, and see how it is applied in industry
- Practice in Python and TensorFlow
- Understand industry best-practices for building deep learning applications
- Get advice from deep learning experts and leaders in the field
- Be able to implement a neural network in TensorFlow
- Understand how to diagnose errors in a machine learning system and prioritise directions for reducing error
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs
Duration : 4 months, 5 hours per week
Rating : 4.8
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This Professional Certificate program in Artificial Intelligence has been created by IBM, the global leader in Tech and one of the pioneers in AI innovation. It is aimed for those who want to learn the skills to work as AI developers. It imparts firm understanding of AI, its applications and use cases. It introduces learners to the concepts and tools like machine learning, data science, natural language processing, image classification, image processing, IBM Watson AI services, OpenCV, and APIs. Students also learn to get started with using pre-built AI smarts without having to create AI models and backends from scratch.
This is a beginner level AI Certification comprising of 6 courses and can be taken by learners with both technical and non-technical backgrounds. The first three courses of the program also constitute the complete AI Foundations For Everyone Specialization. These courses do not require any programming knowledge and have no prerequisites. They are:
- Introduction to Artificial Intelligence (AI) – This is a very popular course and a part of multiple specializations. It introduces the basics of AI and how AI can be used in various industries. It can be taken by everyone whether developers, managers, executives or students.
- Getting Started with AI using IBM Watson – This course introduces learners to various IBM Watson services and APIs and what they can be used for.
- Building AI Powered Chatbots Without Programming – This course teaches how to plan, implement, test, and deploy AI powered chatbots on a website.
The final two courses require some knowledge of Python to build and deploy AI applications. An introductory Python course is included in the program for learners with no programming background. So the remaining 3 courses in the program are the following:
- Python for Data Science and AI – This course covers Python fundamentals, including data structures and data analysis, with complete hands-on exercises.
- Building AI Applications with Watson APIs – In this course, learners utilize multiple Watson AI services and APIs together to build smart and interactive applications.
- Introduction to Computer Vision with Watson and OpenCV – In this course, learners understand Computer vision and its applications, also build and train custom image classifiers using Watson, Python and OpenCV.
The curriculum of this program is very extensive and includes number of hands-on learning projects, including building your own AI chatbot; building, training and testing custom image classifiers; creating a computer vision web application and deploying it to the Cloud.
- Gain the skills to create AI powered applications
- Practice basics of Python and understand how to apply Python programming concepts for data science and AI
- Learn to use IBM Watson AI services and APIs to design, build & deploy AI-powered applications on the web with minimal coding
- Learn how AI-powered chatbot technology works and its applications
- Learn to create and deploy speech enabled virtual assistants with domain intelligence to Facebook etc.
- Explain what computer vision is and its applications
- Especially beneficial for those who want to become builders and developers of AI solutions
- Earn a digital badge from IBM for proficiency in Applied AI in addition to Professional Certificate from Coursera
Duration : 3-6 months, 2-4 hours per week
Rating : 4.6
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This is another popular certificate course in artificial intelligence from IBM. It has been designed to impart the skills and tools necessary for starting a career as an AI or ML Engineer. It is suitable for existing professionals such as AI developers and data scientists who want to level up with machine learning and deep learning skills, as well as students looking to enter the workforce with highly in-demand AI and ML skills.
The program comprises of six self-paced courses that provide learners with a complete understanding of machine learning and deep learning concepts and how to apply them to real world projects. It covers the following topics:
- Develop foundational skills in Machine Learning, and implement supervised and unsupervised machine learning models using Python libraries such as SciPy and ScikitLearn
- Scale machine learning on Big Data using Apache Spark
- Introduction to Deep Learning and Neural Networks
- Discussion of autoencoders, restricted Boltzmann machines, convolutional networks, recursive neural networks and recurrent networks
- Building deep learning models and networks using Keras library
- Using PyTorch library for learning and building deep neural networks
- Working with Tensorflow to develop, tune and deploy deep learning models
- Capstone Project to apply deep learning skills and demonstrate ability to solve real world problems
This IBM AI certificate program takes a very practical and hands-on approach to AI Engineering. All courses have hands-on labs and projects including use cases and real world applications of AI.
This is an intermediate level program and requires prior knowledge and background in certain areas like high school level mathematics, Python programming and using Jupyter notebooks. In addition to these, knowledge of SQL, statistical analysis and some linear algebra are also very helpful. For learners who do not have foundational data science or AI skills, IBM recommends that they first take IBM Applied AI professional certificate or IBM Data Science professional certificate course, before starting this program.
- Curriculum designed by a panel of top IBM experts in the field
- Understand machine learning algorithms including classification, regression, clustering, and dimensional reduction
- Deploy machine learning algorithms and pipelines on Apache Spark
- Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
- Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
- Be able to build, train, and deploy different types of deep architectures
- Demonstrate ability to present and communicate outcomes of deep learning projects
- Option to audit all courses at no charge; verified certificate and IBM badge can be earned at a low monthly fee
Duration : 3-4 months, 12 hours per week
Rating : 4.4
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This Artificial Intelligence Certification program is offered by Columbia University, via the edX platform. It is a very rigorous, graduate-level professional program that represents 25% of the coursework towards a Master’s degree in Computer Science at Columbia.
The program consists of a series of 4 courses that serve as a foundation of expertise in artificial intelligence and machine learning and two of its key applications – robotics and computer animation. These courses are as follows:
- Artificial Intelligence – This course provides an introduction to fundamentals of AI and how to apply them. It teaches how to design intelligent agents or bots that extract data online using certain criteria or keywords.
- Machine Learning – This course teaches the essentials of machine learning and algorithms, including supervised learning techniques for regression and classification, unsupervised learning techniques for data modeling and analysis, probabilistic versus non-probabilistic modelling and optimization and inference algorithms.
- Robotics – This course covers the fundamentals of robotics focusing on both the mind and the body. It teaches the core techniques for representing robots that perform real tasks in the real world.
- Animation and CGI Motion – This course examines the basic rules of motions and how to turn them into computer programs.
Apart from the video lectures, the program includes quizzes, programming assignments, peer-reviewed assignments, and community discussion forums. There is an equal emphasis on theory and practical, with numerous exercises and projects scattered throughout the courses. Learners get to build a basic search agent, AI powered games and linear regression models.
The program assumes a basic understanding of statistics, college level algebra, calculus and knowledge of Python programming language.
The entire program is available for free online, with an option to pay for certification. Learners who subscribe for the paid certificates and successfully complete all courses receive a MicroMasters program certificate from Columbia University.
- Get a solid understanding of the foundational principles of AI
- Learn from experts in the field who teach at Columbia University
- Apply concepts of machine learning to real life problems and applications
- Design and harness the power of Neural Networks
- Learn to design intelligent agents used as news retrieval services, for online shopping and automated tasks
- Explore the applications of AI in fields of robotics, vision and physical simulation
- Exercises and assignments that help to comprehend real world issues and come up with appropriate AI solutions
Duration : 10-12 months, 8-10 hours per week
Rating : 4.6
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Udacity is offering multiple Nanodegree programs in its School of Artificial Intelligence. Nanodegrees are very extensive programs comprising of a larger course of study, usually presented in partnership with leading companies or universities. For those who want to make a career in AI, there are some excellent, powerful, career-centered programs that can be very helpful to advance in the field of AI by spending as little as 8-10 hours per week. There are choices for every level of knowledge and experience from complete beginner focussed programs to those intended for more advanced learners.
Some of the best Udacity AI training programs include:
- AI Product Manager – Covers AI products, creating high quality datasets, training ML models, measuring post-deployment impact and updating models and scaling your AI products.
- Intro to Machine Learning with TensorFlow – Covers foundational machine learning algorithms, supervised models, deep and unsupervised learning, neural network design and training in TensorFlow.
- AI Programming with Python – Covers the essential foundations of AI: the programming tools (Python, NumPy, PyTorch, Anaconda, pandas, and Matplotlib), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
- Artificial Intelligence for Trading – Covers basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Also teaches how to develop trading strategies, and construct a multi-factor model with optimization.
- Computer Vision – Covers computer vision and deep learning techniques—from basic image processing, to building and customizing Convolutional Neural Networks, Recurrent Neural Networks (RNN), Simultaneous Localization and Mapping (SLAM), Object Tracking, Image Classification
- Natural Language Processing – Covers Machine Learning, Speech Recognition, Sentiment Analysis, Machine Translation, Part of Speech Tagging
- Deep Reinforcement Learning – Covers Reinforcement Learning, Neural Networks, PyTorch, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG)
- Artificial Intelligence – Covers AI Algorithms, Search Algorithms, Optimization, Planning, Pattern Recognition
- Machine Learning Engineer – Covers Machine Learning, Supervised Learning, Unsupervised Learning, Deep Learning
- Deep Learning – Covers Deep Learning, Neural Networks, Jupyter Notebooks, CNNs, GANs
- AI for Business Leaders – Covers Artificial Intelligence, Machine Learning, Business Strategy, Data Labeling, Data Modeling
The Nanodegree programs in Udacity’s School of AI are organized around following four main roles or career paths:
- Machine Learning Engineer – Udacity recommends completing following Nanodegree programs in the specified order to start a career in Machine Learning – Intro to Machine Learning with TensorFlow, Intro to Machine Learning with PyTorch, AI Programming with Python, Machine Learning Engineer.
- Deep Learning Engineer – For working as deep learning engineer, following Nanodegree programs are suggested – AI Programming with Python, Machine Learning Engineer, Deep Learning.
- Artificial Intelligence Specialist – The recommended programs in this career path are – Computer Vision, Natural Language Processing, Deep Reinforcement Learning and Artificial Intelligence
- Quantitative Analyst – This career path involves building programming and linear algebra skills, then learning to analyze real data and building financial models for trading. The recommended programs are – AI Programming with Python and Artificial Intelligence for Trading.
- Curriculum designed and delivered by industry experts
- Get practical experience by applying your skills to code exercises and projects
- Get 1-on-1 technical mentor support
- Personal career coach also available for career path guidance
- Complete flexibility with timelines and schedule
Duration : Self-Paced
Rating : 4.6
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This Artificial Intelligence Course from Andrew Ng is largely non-technical and is intended for those who do not need to learn in-depth technicalities of AI but who wish to learn how to make better use of AI in their organizations or roll out AI initiatives or work with an AI team. It is also an excellent course for engineers, programmers and people with technical backgrounds to learn the business aspects of AI. It is very educative and detailed for starters who do not know anything about artificial intelligence.
This AI class starts with a comprehensive overview of what artificial intelligence is and finally goes on to discuss the entire workflow of AI projects and how to develop an AI strategy for your business. It covers the following topics:
- Meaning behind common AI terminology, including machine learning, deep learning, neural networks and data science
- A realistic view of AI and what it can and cannot do with examples
- How to spot opportunities to apply AI to challenges and problems in your organization
- Workflow of machine learning and data science projects
- How to build AI in your company
- Ethical and societal concerns and discussions surrounding AI
This is a 6 hour course that Andrew has developed with business applications in mind, which makes it very unique and one-of-its kind. Plus the fact that it is taught by Andrew himself, a pioneer and huge influencer in the field of artificial intelligence makes the course very popular. It is not restricted to engineers and scientists alone, anybody who sees value in AI and has interest in the subject should take this course.
- Highest rated Coursera Artificial Intelligence online course
- Understand the meanings of various concepts in artificial intelligence and machine learning
- Learn how to work better with an AI team in your organization
- Learn how to chose an AI project
- Get a glimpse into the technical tools used by AI teams
- Case studies related to building an AI product and strategy
- No prerequisites, can be taken by anyone at any level of experience
Duration : 4 weeks, 2 hours per week
Rating : 4.8
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8. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)
TensorFlow is a popular open-source framework for machine learning and probably the best tool you can use to implement machine learning and deep learning algorithms and principles. This TensorFlow course offered on Coursera is a part of TensorFlow in Practice Specialization by deeplearning.ai.
This course is suitable for software developers who have some experience in Python coding and some knowledge of machine learning and deep learning and who want to build scalable AI-powered algorithms in TensorFlow. It teaches how to use TensorFlow to implement the principles of machine learning and deep learning so learners can start building and applying scalable models to real-world problems.
The course comprises of 4 weekly modules that take learners from basic to mastery of TensorFlow. They cover the following topics:
- Introduction to what Machine Learning and Deep Learning are
- Introduction to Computer Vision
- Coding a Computer Vision Neural Network
- Introduction to Convolutional Neural Networks and Pooling
- Implementing convolutional layers and pooling layers
- Understanding ImageGenerator
- How to handle complex real-world images
There are abundant coding examples and programming assignments throughout the course. By the end of the course learners are able to gain practical skills to come up with scalable solutions to real-life AI challenges.
The course is taught by Lawrence Moroney, an AI advocate at Google. He has authored over 30 programming books and several science fiction novels.
- Learn to apply TensorFlow skills to a wide range of problems and projects
- Learn the best practices for using TensorFlow
- Build a basic neural network in TensorFlow
- Understand how to use convolutions to improve your neural network
- Train a neural network for a computer vision application
Duration : 4 weeks, 6-9 hours per week
Rating : 4.7
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The path of learning Artificial Intelligence is often overwhelming with complex maths and technical topics. This Udemy AI course by Kirill Eremenko and Hadelin de Ponteves attempts to break that trend by offering an intuitive and exciting approach that guides learners into exploring the world of AI. It teaches how to combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications.
The course is created on the theory that Games are the simplest test environment for AI, and when an algorithm can beat a game, it is proof that same principles can be applied to real world challenges. Therefore, the course uses a simulated AI environment, OpenAI Gym (a project backed by entrepreneurs like Elon Musk and Peter Thiel) in order to learn how to create artificial intelligence which surpasses humans in games like Doom and Breakout.
The course is divided into 3 modules, each one taking a unique AI creation process of different difficulty, structure and purpose:
- Module 1 – Create AI to conquer the game of Breakout
- Module 2 – Create a more complex AI to pass a level in Doom
- Module 3 – Build an AI for self-driving cars
This is a completely hands-on course that takes learners through the practical steps necessary to be able to code self-improving AI for a range of purposes. Every tutorial starts with a blank page and the instructors write the code from scratch. This way the learners are able to follow along better and understand exactly how the code comes together and what each line means. No previous coding experience using Python is required.
The course also covers Q-learning, which is a form of machine learning based on reinforcement learning, and is being used in a lot of cutting-edge applications.
- Beginner friendly course to learn the fundamentals of AI, both the theory as well as its practical applications
- Get skilled to build AI adaptable to any environment in real life
- Master the State of the Art AI models
- Make a virtual Self Driving Car
- Make an AI to beat games
- Explore Q-Learning, Deep Q-Learning and Deep Convolutional Q-Learning
- Understand how to merge AI with OpenAI Gym to learn as effectively as possible
- In-course support from an expert team of professional Data Scientists
- Get downloadable Python code templates for every AI you build in the course
- Content focused on building up learner’s intuition in coding AI that leads to better learning outcomes
Duration : 16.5 hours on-demand video
Rating : 4.3
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Reinforcement Learning is an entirely different paradigm in AI and Machine Learning. It has given us amazing insights both in behavioral psychology and neuroscience, and is the closest thing we have so far to a true general artificial intelligence. This course is one of the best AI courses out there on Reinforcement Learning. It gives learners a primer on AI-powered reinforcement learning, with a particular focus on stock trading and online advertising. It gives insights into AI techniques that one would never see in traditional supervised machine learning, unsupervised machine learning, or even deep learning.
This course is best fit for those who already have basic knowledge of theoretical and technical aspects of AI and want to understand Reinforcement learning thoroughly. Since it teaches advanced level concepts, the students are expected to know Calculus, Probability, Object-oriented programming, Python coding, Numpy coding, Linear regression, Gradient descent etc.
In this AI class students understand reinforcement learning on a technical level. Following topics are covered in the course content:
- 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 (MDPs)
- Dynamic Programming
- Monte Carlo
- Temporal Difference (TD) Learning (Q-Learning and SARSA)
- Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)
There is also a Project where learners get to apply Q-learning to build a stock trading bot, and another about building AI for the Tic-Tac-Toe game.
- Best online AI course for those looking to gain knowledge of Python-based AI reinforcement learning
- Understand the relationship between reinforcement learning and psychology
- Apply gradient-based supervised machine learning methods to reinforcement learning
- Implement 17 different reinforcement learning algorithms
- Range of exercises and assignments for hands-on practice
Duration : 12.5 hours on-demand video
Rating : 4.6
This article is republished from CodeSpaces.