Saya baru nemu kumpulan materi kuliah machine learning dari beberapa kampus. Materinya rata-rata dishare di Youtube.

Stanford

materinya:

• Linear Regression and Gradient Descent
• Logistic Regression
• Naive Bayes
• SVMs
• Kernels
• Decision Trees
• Introduction to Neural Networks
• Debugging ML Models

Andrew NG

Google Cloud- making friends with machine learning

• Explainability in AI
• Classification vs. Regression
• Precession vs. Recall
• Statistical Significance
• Clustering and K-means
• Ensemble models

dari Cassie Kozyrkov

Cornell-Applied Machine Learning

• Optimization and Calculus
• Overfitting and Underfitting
• Regularization
• Monte Carlo Estimation
• Maximum Likelihood Learning
• Nearest Neighbours

https://github.com/kuleshov/cornell-cs5785-2020-applied-ml

Tuebingin: Intro to machine learning

• Linear regression
• Logistic regression
• Regularization
• Boosting
• Neural networks
• PCA
• Clustering 

https://dkobak.github.io

Munich

• Machine Learning Basics
• Supervised Regression and Classification
• Performance Evaluation
• Classification and Regression Trees (CART)
• Information Theory
• Linear and Nonlinear Support Vector Machine
• Gaussian Processes

https://introduction-to-machine-learning.netlify.app

Tuebingen: ML & statistik

http://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/

• KNN
• Bayesian decision theory
• Convex optimization
• Linear and ridge regression
• Logistic regression
• SVM
• Random Forests
• Boosting
• PCA
• Clustering …

Tuebingen: Probabilistik ML

• Reasoning about uncertainty
• Continuous Variables
• Sampling
• Markov Chain Monte Carlo
• Gaussian Distributions
• Graphical Models
• Tuning Inference Algorithms …

https://uni-tuebingen.de/en/134452

Semoga Bermanfaat!

Referensi:

https://github.com/dair-ai/ML-YouTube-Courses