Artificial intelligence (AI) is one of the most exciting and rapidly advancing fields in technology today. With AI powering everything from virtual assistants like Siri to self-driving cars, the possibilities seem endless. For those interested in getting involved with this groundbreaking field, learning AI online has never been easier or more accessible.
From free online courses offered by leading universities to do-it-yourself YouTube tutorials, there are now abundant resources available to pick up AI skills without needing to enroll in a formal degree program.
In this article, we will explore the various options for learning artificial intelligence online, from structured courses to self-guided learning. We will look at the pros and cons of each approach, resources available, and what you can hope to gain from studying AI in an online format. Whether you are a complete beginner looking to gain foundational knowledge or a professional hoping to add AI skills to your resume, this guide will help you identify the best online learning opportunities in artificial intelligence.
Structured Online Courses
For those seeking a comprehensive, structured approach to learning AI, online courses offered by various platforms are a great option. These courses provide expert-designed curriculums, graded assignments, projects, and certificates of completion. Some of the major providers offering AI courses online include:
Coursera partners with over 200 leading universities and companies worldwide to offer high-quality online courses. Their AI catalog includes:
- Machine Learning by Stanford University – This foundational course provides a broad introduction to supervised and unsupervised machine learning algorithms like linear regression, logistic regression, K-means clustering, neural networks, and more. Students learn both the core concepts and how to implement models in Python.
- Deep Learning Specialization by DeepLearning.AI – This 5-course specialization from AI expert Andrew Ng provides a comprehensive introduction to deep learning and neural networks. The courses cover foundational theory along with hands-on Python and TensorFlow coding projects.
- AI for Everyone by DeepLearning.AI – This beginner-friendly course taught by Andrew Ng aims to provide a non-technical overview of AI concepts and applications in business and society. It offers an accessible starting point for understanding AI without programming requirements.
edX offers affordable, university-level AI courses including:
- ColumbiaX: Artificial Intelligence (AI) – This course surveys key techniques like machine learning, knowledge representation, probabilistic reasoning, robotics, natural language processing, and computer vision. Students learn both theory and applications across industries.
- Microsoft Professional Program for Artificial Intelligence – This program includes 10 courses focused on building applied AI skills using Microsoft tools like Azure Machine Learning and Cognitive Toolkit. The curriculum covers data science, computer vision, conversational AI, and more.
- IBM AI Engineering Professional Certificate – This 6-course program from IBM teaches hands-on skills in deep learning and machine learning using Python, TensorFlow, and other libraries. Courses cover computer vision, NLP, chatbots, reinforcement learning, and other topics.
Udacity “nanodegree” programs focus on building in-demand job skills. For AI, they offer:
- Artificial Intelligence Nanodegree – This program covers AI algorithms, neural networks, machine learning techniques, and implementing models in Python. The curriculum includes building a portfolio of projects.
- Computer Vision Nanodegree – Students learn in-depth computer vision techniques like object classification, detection, tracking, instance segmentation, and GANs for image generation.
- Natural Language Processing Nanodegree – This program covers NLP applications like sentiment analysis, named entity recognition, topic modeling, text summarization, machine translation, and building chatbots.
Udemy offers a large catalog of AI courses created by individual instructors. Top offerings include:
- Machine Learning A-ZTM: Hands-On Python & R In Data Science – This highly-rated introductory course covers supervised and unsupervised machine learning algorithms, implemented hands-on in Python and R.
- Advanced AI: Deep Reinforcement Learning in Python – This course helps students master deep Q-learning, policy gradients, and other reinforcement learning techniques, with coding examples in Python.
- AI for Business Leaders: Boosting Companies with Artificial Intelligence – This course provides executives and managers an overview of how to apply AI in organizations for competitive advantage.
The advantages of structured courses include organized curriculums designed by experts, hands-on practice, graded assignments, and credentials for your resume. Downsides can include required time commitments and fees to access materials. But quality courses offer robust foundations in key AI areas.
For flexible, self-paced learning, there is a wealth of free AI educational content online. Options include:
Many experts have created YouTube channels explaining AI concepts and code:
- Siraj Raval – This channel offers enthusiastic explanations of AI fundamentals, ethics, philosophy, and coding tutorials focused on hands-on projects.
- Sentdex – With over 300 tutorials, this channel features Python code walkthroughs for machine learning algorithms, neural networks, computer vision techniques, and more.
- DeepLearning.TV – This channel provides access to paid tutorial courses, but also makes some machine learning and deep learning videos available for free.
- Intel Nervana – Intel’s AI education initiative provides tutorial videos explaining neural networks, deep learning techniques, AI frameworks like TensorFlow, and more.
Online Books & Textbooks
Many seminal AI textbooks are free online:
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig – This leading textbook surveys AI techniques like informed search, logic, knowledge representation, probabilistic reasoning, machine learning, neural networks, and robotics.
- Neural Networks and Deep Learning by Michael Nielsen – This online book offers a deep dive into neural networks, including derivations of core math and coding examples in Python.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville – This textbook provides advanced coverage of deep learning, with extensive math and academic research references.
- Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto – This foundational textbook explains reinforcement learning techniques like dynamic programming, temporal difference, and Q-learning.
Schools like MIT and Stanford provide free access to materials from actual AI courses:
- CS221: Artificial Intelligence: Principles and Techniques – Stanford’s intro AI course covers search, logic, planning, knowledge representation, Markov decision processes, robotics, and machine learning.
- 6.034 Artificial Intelligence – Materials from MIT’s graduate-level AI course taught by AI pioneer Patrick Winston, including lecture videos on AI principles, Lisp coding, search, and knowledge representation.
- CS 229 Machine Learning – This legendary Stanford course taught by Andrew Ng provides lecture videos, notes, and assignments on supervised and unsupervised machine learning topics.
Self-guided learning allows you to direct your own studies. But lacking structure, credentials, or guidance can be downsides to weigh.
Getting Hands-On Experience
While online courses and materials can provide a theoretical foundation, gaining practical experience applying AI techniques is essential. There are a few ways to get hands-on with artificial intelligence online:
- AI Experimenter – Interactive browser-based experience from Google to build, train, and experiment with different AI models like image classifiers.
- AI Gym – Set of browser-based environments for testing reinforcement learning algorithms on games and other simulated tasks.
- Kaggle – Platform that hosts machine learning competitions and datasets that allow you to practice building models.
- Programming Projects – Sites like GitHub have many sample AI projects in Python and other languages that you can run and experiment with.
- AI Apprenticeship – Programs like Udacity’s that connect you with mentors for guiding you through real-world AI projects.
Getting hands-on experience will allow you to demonstrate applied skills and have projects to show employers. Starting out experimenting with pre-made projects and models can provide an approachable on-ramp before developing your own applications.
The world of artificial intelligence is rapidly changing, creating high demand for AI skills. The good news is that with abundant online courses, textbooks, tutorials, and hands-on resources, becoming AI-literate has never been more accessible.
Whether you opt for the guided structure of a university-level course or the flexibility of self-directed learning, you can gain a solid grounding in areas like machine learning, neural networks, and natural language processing completely online.
Combining theoretical knowledge with practical experience applying AI gives you the best chance of developing expertise. With patience and persistence, taking the self-learner route can prove a rewarding entry point to contributing to the future of artificial intelligence.
Mentioned In The Article
Online learning platform that partners with universities and companies to offer over 5,000 courses in topics including artificial intelligence.
A major MOOC provider, offering affordable online courses in AI and other subjects from top universities like MIT and Stanford.
Platform focused on “nanodegree” programs that teach hands-on technology skills like AI, machine learning, data science, and more.
A marketplace for online courses with a wide selection of AI classes, varying in quality, created by individual instructors.
YouTuber and AI educator, known for his enthusiastic and accessible explanations of AI concepts with a focus on hands-on projects.
YouTube channel featuring Python coding tutorials focused on teaching machine learning, computer vision, NLP, and other AI techniques.
YouTube channel and website that makes video courses and other AI learning resources available for free.
Intel’s AI and deep learning education initiative, including tutorial videos on topics like neural networks, frameworks, and applications.
Stuart Russell and Peter Norvig
AI researchers and professors who authored Artificial Intelligence: A Modern Approach, the leading textbook used in AI university courses.
Author of the online book Neural Networks and Deep Learning which provides a deep dive into neural networks, including some math and Python code.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville
AI research pioneers who authored the leading textbook Deep Learning, which covers deep neural networks with advanced math.
Richard Sutton and Andrew Barto
Seminal researchers in reinforcement learning who authored the book Reinforcement Learning: An Introduction which teaches foundational RL algorithms.
Interactive web-based portal created by Google that allows you to build, train, and run AI models like image classifiers.
A website providing simulated environments to test out reinforcement learning algorithms through your browser.
Popular platform hosting competitions, datasets, and computing resources for practicing machine learning and data science skills.
Code hosting site with sample machine learning and AI projects in Python and other languages that you can download and experiment with.
Udacity AI Apprenticeship
Udacity program that matches learners with mentors from industry to guide them through building applied AI portfolio projects.
Additional Resources for Learning AI Online
The online courses, textbooks, tutorials, and hands-on tools discussed so far provide a wealth of materials to start developing artificial intelligence skills and knowledge on your own. Here are some additional learning resources and communities worth exploring:
AI podcasts provide a great option to passively learn while commuting or exercising:
- Artificial Intelligence Podcast – Interviews AI researchers and covers developments in neural networks, robotics, philosophy, and more.
- Talking Machines – Conversations with machine learning experts from leading companies and academics.
- Linear Digressions – Discusses practical applications of machine learning and data science.
Connecting with other AI learners and experts can accelerate your learning:
- Reddit MachineLearning – Active community of over 1 million members discussing the latest in machine learning.
- KDnuggets – Leading data science and analytics community with active AI and machine learning forums.
- Fast.ai Forums – Discussion forums for courses and topics related to deep learning, NLP, computer vision, and more.
Conferences & Events
Look for local AI meetups or virtual conferences to attend and learn from presentations:
- O’Reilly AI Conference – Leading academic and industry AI conference running since 2016.
- AI Summit – Large AI business conference organized annually in NYC.
- NeurIPS – Top academic machine learning conference (limited free virtual access).
These resources provide guidance on getting AI jobs and advancing in your career:
- 80,000 Hours AI Career Guide – Provides analysis of AI career paths and how to develop the necessary skills.
- Springboard AI Career Track – Structured mentorship program focused on transitioning into AI engineering roles.
Overall, learning artificial intelligence online requires leveraging diverse resources spanning structured courses, textbooks, community forums, hands-on practice, and career guidance. Taking a multifaceted approach will allow you to most effectively and efficiently pick up modern AI skills, knowledge, and real-world applications. With the abundance of materials now available online, the path to mastering artificial intelligence has never been more accessible.
With a passion for AI and its transformative power, Mandi brings a fresh perspective to the world of technology and education. Through her insightful writing and editorial prowess, she inspires readers to embrace the potential of AI and shape a future where innovation knows no bounds. Join her on this exhilarating journey as she navigates the realms of AI and education, paving the way for a brighter tomorrow.