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

### General Machine Learning with Python and Scikit-learn

- Machine Learning with scikit-learn, Data School
- Machine Learning with scikit-learn, Jake Vanderplas
- Decision Trees, The Grimm Scientist
- Machine Learning with scikit-learn, Andreas Mueller
- Convolutional Neural Networks with Python, Stanford

### 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
- 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

### 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