01 Beginner
Introduction to Machine Learning
11 lessons
Curriculum
Machine Learning and Data Science
Reading the number a model produces together with the number a procedure dumber than it produces on the same measure: the labels each kind of learning asks for, the two separate numbers measured on the training set and on a held-out set, the difference a feature adds against what leakage inflates, the share of the model family and the tuning budget, what the baseline is when there are no labels, what capacity adds and what training practice carries, the baselines of the language and vision domains, what a random policy and the most popular recommendation come to, and who the baseline was chosen for.
Reading the number a model produces together with the number a procedure dumber than it produces on the same measure: the labels each kind of learning asks for, the two separate numbers measured on the training set and on a held-out set, the difference a feature adds against what leakage inflates, the share of the model family and the tuning budget, what the baseline is when there are no labels, what capacity adds and what training practice carries, the baselines of the language and vision domains, what a random policy and the most popular recommendation come to, and who the baseline was chosen for.
Courses
02 Intermediate
Data Preparation and Feature Engineering
12 lessons
03 Intermediate
Supervised Learning
20 lessons
04 Intermediate
Unsupervised Learning
10 lessons
05 Advanced
Deep Learning
24 lessons
06 Advanced
Natural Language Processing and Computer Vision
14 lessons
07 Advanced
Reinforcement Learning and Recommender Systems
14 lessons
08 Advanced
Model Evaluation, Interpretability and Ethics
15 lessons