YouTube playlist

Currently, the course is in self-paced mode, in this short intro we go through roadmap and discuss how to best approach the course material.

Introductionvideo, slides

  1. Exploratory data analysis with Pandas – video
  2. Visualization, main plots for EDA – video
  3. Decision trees – theory and practical part
  4. Logistic regression – theoretical foundations, practical part (baselines in the “Alice” competition)
  5. Ensembles and Random Forest – part 1. Classification metrics – part 2. Example of a business task, predicting a customer payment – part 3
  6. Linear regression and regularization – theory, LASSO & Ridge, LTV prediction – practice
  7. Unsupervised learning – Principal Component Analysis and Clustering
  8. Stochastic Gradient Descent for classification and regression – SGD, and Vowpal Wabbit
  9. Time series analysis with Python (ARIMA, Prophet) – video
  10. Gradient boosting: basic ideas – part 1, key ideas behind major implementations: Xgboost, LightGBM, and CatBoost + practice – part 2

Outroductionvideo, slides

“Jump into Data Science” – this video will walk you through the preparation process for your first DS position once basic ML and Python are covered. Slides