Assuming you are starting from scratch, here is a short list of resources that will get you started. They are organized by level of complexity, and they cover both the general aspects of Machine Learning, Computer Vision, as well as more deep technical concepts and algorithms.
If you don't already, get to know Andrew Ng. Then read and watch everything he publishes.
Introduction to Machine Learning Problem Framing — A 1-hour course designed for absolute beginners in the field and perfect if you are just starting. It's specially useful to help you identify opportunities to apply Machine Learning.
Machine Learning Recipes - Free 10-episode YouTube series developed by Google, introducing Machine Learning development for a beginner audience.
Machine Learning A-Z™: Hands-On Python & R In Data Science — This is an introductory Udemy Machine Learning course where you'll learn to create Machine Learning Algorithms in Python and R.
Machine Learning Clash Course — 25 lessons that expand 15 hours. 40+ exercises, lectures from Google researchers, real-world case studies, and interactive visualizations of algorithms in action. Recommended if you want an end-to-end course covering Machine Learning at a deeper level.
Data Preparation and Feature Engineering in ML — Recommended for intermediate technical people that want to focus on the data preparation and feature engineer side of Machine Learning. This course assumes that you completed the Machine Learning Clash Course.
Deep Learning Specialization — This Coursera course is taught by Andrew Ng. You will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will master not only the theory, but also see how it is applied in industry.
Data Scientist Nanodegree — This Udacity Nanodegree covers building effective machine learning models, running data pipelines, building recommendation systems, and deploying solutions to the cloud with industry-aligned projects.
Deep Learning Nanodegree — In this Udacity Nanodegree you will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment.
Image Classification — Learn how Google developed the state-of-the-art image classification model powering search in Google Photos. Get a crash course on convolutional neural networks, and then build your own image classifier to distinguish cat photos from dog photos. Programming experience is required.
Convolutional Neural Networks for Visual Recognition — This is Standford's CS231n course, published free in YouTube, and covering the state-of-the-art Computer Vision theory.
Computer Vision Nanodegree — In this Udacity Nanodegree you will learn cutting-edge computer vision and deep learning techniques, from basic image processing, to building and customizing convolutional neural networks. You will apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects.
- Understanding Machine Learning: From Theory to Algorithms
- Deep Learning (Adaptive Computation and Machine Learning series)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Deep Learning for Computer Vision
- Machine Learning
- Computer Vision: Algorithms and Applications
- Programming Computer Vision with Python
- Data Skeptic — Weekly podcast featuring short mini-episodes explaining high level concepts in data science, and longer interview segments with researchers and practitioners.
- Data Science Imposters — Weekly podcast covering data science, analytics, big data, machine learning, and artificial intelligence topics.
- Linear Digressions - Technical weekly podcast covering data science, machine learning, and artificial intelligence.
People to Follow
- Andrew Ng - Co-Founder of Coursera; Stanford CS adjunct faculty. Former head of Baidu AI Group/Google Brain.
- Fei-Fei Li - Stanford CS Professor, Co-Director of the Stanford Human-Centered AI Institute, Co-Founder/chair @ai4allorg, researcher of Artificial Intelligence, Computer Vision, and Machine Learning.
- Andrej Karpathy - Director of AI at Tesla. Previously a Research Scientist at OpenAI, and CS PhD student at Stanford.
- Ian Goodfellow — Google Brain research scientist leading a team studying adversarial techniques in AI. Lead author of http://www.deeplearningbook.org.
- François Chollet — Deep learning at Google. Creator of Keras, neural networks library. Author of "Deep Learning with Python".
- Russ Salakhutdinov — Professor at Carnegie Mellon University, Director of AI Research at Apple.
- Charles Isbell — Professor and Executive Associate Dean College of Computing Georgia Tech.
- Michael Littman — Professor at Brown University. Works mainly in Reinforcement Learning.
- Oriol Vinyals - Research Scientist, Machine Learning/Deep Learning/AI, Google DeepMind. Previously worked on Google Brain.
- Soumith Chintala — Works at Facebook AI Research. Created PyTorch. Co-authored WGAN and DCGAN research papers.