Discover how you can become a machine learning engineer with free and paid online resources.

Machine Learning Resources

These are the resources you can use to become a machine learning or deep learning engineer. All of the resources are available for free online. Please check their respective licenses.

Machine Learning Theory

• Machine Learning, Stanford University
• Machine Learning, Carnegie Mellon University
• Machine Learning, MIT
• Machine Learning, California Institute of Technology
• Machine Learning, Oxford University
• Machine Learning, Data School

Deep Learning Theory

• Deep Learning, Ian Goodfellow
• Neural Networks and Deep Learning
• Understanding LSTM Networks
• Deep Residual Learning

Forward and Backpropagation Theory and Code

• Step by Step Forwardpropagation and Backpropagation with Numbers
• Full Manual Backward Propagation with TensorFlow
• Reverse Mode Automatic Differentiation with TensorFlow
• Simple Backward Propagation with Python
• Backward Propagation from Scratch with Python
• Neural Networks Demystified with Python, Welch Labs
  • Part 1: Data and Architecture
  • Part 2: Forward Propagation
  • Part 3: Gradient Descent
  • Part 4: Backward Propagation
  • Part 5: Numerical Gradient Checking
  • Part 6: BFGS Numerical Optimization Algorithm
  • Part 7: Overfitting, Testing and Regularization

General Machine Learning with Python and Scikit-learn

• Machine Learning with scikit-learn, Data School
  • Machine Learning with scikit-learn
  • Comparing Supervised Learning Algorithm
  • Machine Learning with text
• Machine Learning with scikit-learn, Jake Vanderplas
  • Introduction to scikit-learn
  • Basic Principles
  • Linear Regression
  • Support Vector Machines
  • Regression Forests
  • Dimensionality Reduction
  • k-means Clustering
  • Density Estimation
  • Validation and Model Selection
• Decision Trees, The Grimm Scientist
• Machine Learning with scikit-learn, Andreas Mueller
  • Introduction to Machine Learning with Python
  • Scikit-learn tutorial
  • Advanced scikit-learn
• Convolutional Neural Networks with Python, Stanford
  • CS231n Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks with TensorFlow/Keras

• Deep Learning Models like VGG, Inception V3, ResNet and more in Keras
• Practical Deep Learning with Keras, Jason Brownlee
• Wide Residual Networks in Keras
• Wide ResNet in TensorLayer
• TensorLayer Official Tutorials

Reinforcement Learning Theory

• Reinforcement Learning Introduction, Nervana
• Reinforcement Learning, Sutton
  • Programming Exercises for Sutton’s Book
• Uncertainty Estimates from Dropouts

Reinforcement Learning with TensorFlow/Keras

• Using Keras with DPPG to play TORCS
• Advantage async actor-critic Algorithms (A3C) and Progressive Neural Network in TensorFlow

Recurrent Neural Networks Theory

• Understanding LSTMs

Recurrent Neural Networks with TensorFlow

• RNN Official TensorFlow Tutorials
• RNN-LSTM with TensorFlow
• Introduction to RNN in TensorFlow
• Advanced RNN guides and code
• RNN in TensorFlow with and without API
• RNNs in TensorFlow, A Practical Guide and Undocumented Features
• TensorFlow code for Latest RNN Papers

Mathematics Useful for Machine Learning

• Discrete Mathematics, MIT
• Linear Algebra, MIT
• Linear Algebra Review, Stanford
• Probability Review, Stanford
• Convex optimization overview, Stanford
• More convex optimization overview, Stanford
• Single Variable Calculus, MIT
• Practical Guide for Matrix Calculus for Deep Learning

Deep Learning Environment

• One-click AWS AMI with TensorFlow and Keras

Tags: machine_learning