Machine Learning: 2017 Edition
Course Materials
This course is being taught by Jeremy Howard, and was developed by Jeremy along with Rachel Thomas. Jeremy used a top-down teaching method, which is different from how most math courses operate. If you took the fast.ai deep learning part 1 2018 edition course, that is what we used. The course used a specific library (fastai) built on top of PyTorch. The first part of the course focused on Decision Trees with Random Forests, the second part on Neural Networks for Deep Learning. The course goes at a somewhat gentler pace, but doesn't show how to build world-class models (the focus is more on process and interpretation, and also more in depth discussion of foundational details). The Deep Learning course is more intense, and gets you building state of the art models from lesson 1. Both can be understood on their own, but they both support each other.
Lessons Cover
Introduction to Random Forests
Random Forest Deep Dive
Feature Engineering
Random Forest Interpretation
Train vs Test
What is Machine Learning
Why Do We Use Machine Learning
Decision Trees Ensemble
Basic Neural Networks
SGD
Backpropagation
Neural Networkwork Training
Broadcasting
PyTorch
Logistic Regression
Natural Language Processing (NLP)
Naive Bayes
Structured and Time-Series Data
Entity Embeddings
Data Science and Ethics
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