Machine Learning Part 2 – Unsupervised Learning

Advanced ensemble techniques: Gradient Boosting, XGBoost, and how to perform hyperparameter tuning with GridSearch and RandomSearch.

1. 1Boosting and Hyperparameter Tuning Part 1Free
2. 2Boosting and Hyperparameter Tuning Part 2

Introduction to unsupervised learning and clustering algorithms: K-Means, hierarchical clustering, and cluster quality evaluation.

1. 1Intro to Unsupervised, Clustering Part 1Free
2. 2Intro to Unsupervised, Clustering Part 2
3. 3Intro to Unsupervised, Clustering Part 3
4. 4Intro to Unsupervised, Clustering Part 4
5. 5Intro to Unsupervised, Clustering Part 5

Building recommendation systems: collaborative filtering, content-based filtering, and hybrid approach.

1. 1Recommender System Part 1Free
2. 2Recommender System Part 2
3. 3Recommender System Part 3
4. 4Recommender System Part 4

Dimensionality reduction techniques with PCA: eigenvalues/eigenvectors concepts, variance explained, and applying PCA in ML pipelines.

1. 1Principal Component Analysis Part 1
2. 2Principal Component Analysis Part 2

Exercises and unsupervised learning portfolio project: apply clustering, recommender systems, and PCA on real case studies.

1. 1Exercise and Project Portfolio 8