Mathew K AnalyticsML algorithms deep dive
A free 12-lesson course. Every lesson has a video walkthrough and a Jupyter notebook you can download or open in Google Colab; 12 lessons include real datasets. Tools covered: scikit-learn, pandas, NumPy, Matplotlib, SciPy, statsmodels.
25 minLesson 1
Linear Regression from Scratch in Python | ML Algorithms #1
21 minLesson 2
Logistic Regression for Classification | ML Algorithms #2
20 minLesson 3
K-Nearest Neighbours (KNN) Explained | ML Algorithms #3
17 minLesson 4
Decision Trees: How They Really Work | ML Algorithms #4
17 minLesson 5
Random Forests Explained with Python | ML Algorithms #5
17 minLesson 6
Gradient Boosting Explained Step by Step | ML Algorithms #6
17 minLesson 7
Support Vector Machines Made Intuitive | ML Algorithms #7
16 minLesson 8
Naive Bayes Classifier Explained | ML Algorithms #8
19 minLesson 9
K-Means Clustering in Python | ML Algorithms #9
15 minLesson 10
Hierarchical Clustering & Dendrograms | ML Algorithms #10
15 minLesson 11
PCA & Dimensionality Reduction Explained | ML Algorithms #11
15 minLesson 12