In this post, you will learn about the **free course on machine learning** (STAT 451) recently taught at **University of Wisconsin-Madison by Dr. Sebastian Raschka**. Dr. Sebastian Raschka in currently working as an assistant Professor of Statistics at the University of Wisconsin-Madison while focusing on deep learning and machine learning research.

The course is titled as “**Introduction to Machine Learning”. **The recording of the course lectures can be found on the page – Introduction to machine learning.

The course covers some of the following topics:

- What is machine learning?
- Nearest neighbour methods
- Computational foundation
- Python Programming (concepts)
- Machine learning in Scikit-learn

- Tree-based methods
- Decision trees
- Ensemble methods

- Model evaluation techniques
- Concepts of overfitting & underfitting
- Resampling methods
- Cross-validation methods
- Statistical tests & algorithm selection
- Evaluation metrics

By far, these are one of the **best lectures on machine learning, **I have come across on the internet. You can find some other useful links such as the following in relation to Dr. Sebastian Raschka

- Books such as Python Machine Learning (3rd Edition)
- Deep learning lectures
- A collection of mathematics resources which may prove to be useful while learning machine learning
- Python notebooks for machine learning
- Some great reads (blogs) on varied topics of machine learning. Here are
**my favorites**- Model evaluation, model selection, and algorithm selection in machine learning Part I – The basics
- Model evaluation, model selection, and algorithm selection in machine learning Part II – Bootstrapping and uncertainties
- Model evaluation, model selection, and algorithm selection in machine learning Part III – Cross-validation and hyperparameter tuning

- Positively Skewed Probability Distributions: Examples - March 21, 2023
- Maximum Likelihood Estimation: Concepts, Examples - March 20, 2023
- Generative Modeling in Machine Learning: Examples - March 19, 2023

[…] The intuition behind bias and variance can be understood based on the following diagram. One can get a detailed explanation by going through the free online course – Introduction to Machine Learning by Dr. Sebastian Raschka. […]