_While making_ [_GeekForge_](https://geekforge.io/) _— a daily listing of interesting coding tasks — we researched several sources where you can learn AI and ML, and we thought it would be a good idea to share this list with you._\n\nTwo years have already passed since Mark Cuban said that if you don’t understand artificial intelligence, deep learning, and machine learning “[you’re going to be a dinosaur within three years](https://bothsidesofthetable.com/mark-cuban-on-why-you-need-to-study-artificial-intelligence-or-youll-be-a-dinosaur-in-3-years-db3447bea1b4).” If you still didn’t dig yourself into that knowledge, especially if you’re a developer, then you’ve got about a year left to see whether he was right or not.\n\nBut luckily for you, if you are in fact interested in keeping your skills up to date, I hand-picked the best resources that are relevant today, regardless if you’re a beginner in the field or if you’ve already got your feet wet a long time ago. From video courses and books to interactive classes and coding tasks, within this list you will find the way to keep yourself out of the prehistoric era!\n\n#### [Introduction to Machine Learning Problem Framing from Google](https://developers.google.com/machine-learning/problem-framing/)\n\nThis one-hour course introduces the machine-learning mindset and helps you identify the appropriate situations for machine learning.\n\n#### [Artificial Intelligence: Principles and Techniques from Stanford University](http://web.stanford.edu/class/cs221/)\n\nThis prepares students to make meaningful contributions to society as engaged citizens and leaders in a complex world.\n\n#### [Daily email list of AI and ML coding tasks from GeekForge](https://geekforge.io/)\n\nYou can solve tasks independently or discuss them with the community. It’s the best way to educate yourself on new technology and build a portfolio of your completed tasks.\n\n#### [CS405: Artificial Intelligence from Saylor Academy](https://learn.saylor.org/course/view.php?id=96)\n\nMaterials on AI programming and ML (machine learning) introduce you to their applications to computational problems and understanding intelligence.\n\n#### [Intro to Artificial Intelligence at Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)\n\nThis course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.\n\n#### [CS188 Intro to AI from UC Berkeley](http://ai.berkeley.edu/lecture_videos.html)\n\nUC Berkeley was born out of a vision in the State Constitution for a university that would “contribute even more than California’s gold to the glory and happiness of advancing generations.”\n\n#### [Artificial Intelligence course at edX](https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-0)\n\nLearn the fundamentals of AI and apply them. Design intelligent agents to solve real-world problems including search, logic, and constraint satisfaction problems.\n\n#### [Artificial Intelligence course from MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)\n\nThis course includes interactive demonstrations that are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.\n\n#### [Artificial Intelligence A-Z: Learn How To Build An AI at Udemy](https://www.udemy.com/artificial-intelligence-az/)\n\nCombine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications.\n\n#### “[Artificial Intelligence: A Modern Approach” at Amazon](https://www.amazon.com/gp/product/0137903952)\n\nThis best-selling book offers the most comprehensive, up-to-date introduction on the theory and practice of artificial intelligence\n\n#### “[Foundations of Statistical Natural Language Processing” at Amazon](https://www.amazon.com/gp/product/0262133601)\n\nStatistical approaches to processing natural language text have become dominant during the recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear.\n\n#### [Machine Learning at Coursera](https://www.coursera.org/learn/machine-learning)\n\nThis course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.\n\n#### [Machine Learning and AI Foundations: Classification Modeling at Lynda.com](https://www.lynda.com/course-tutorials/Machine-Learning-AI-Foundations-Classification-Problems/645050-2.html)\n\nThis course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy for their projects.\n\n#### “[Machine Learning” at Amazon](https://www.amazon.com/gp/product/0071154671)\n\nThis book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience.\n\n#### [Deep Learning in Neural Networks from the University of Lugano](https://arxiv.org/pdf/1404.7828v4.pdf)\n\nThis historical survey compactly summarizes relevant work on deep artificial neural networks, which have won numerous contests in pattern recognition and machine learning.\n\n#### [Grokking Deep Learning in Motion by Manning](https://www.manning.com/livevideo/grokking-deep-learning-in-motion)\n\nGrokking Deep Learning in Motion is a new live video course that takes you on a journey into the world of deep learning.\n\nNo matter what your prior experience is, the fact that you can learn the basics of the most important technologies in the world, like artificial intelligence and machine learning, to improve your coding skill set could place you above your peers in no time. Any of the following resources could be a starting point. Which one will it be for you? Ordering one of the books, enrolling in a university course, or maybe just signing yourself in for the daily tasks on [GeekForge](http://geekforge.io). Any of these options are better than doing nothing.