> ## Content Index
> Fetch the complete content index at: https://bioinformatics.ghost.io/llms.txt
> Use this file to discover other available public pages before exploring further.

# Machine Learning Resources
- URL: https://bioinformatics.ghost.io/ml-resources/
- Published: 2018-08-13T01:05:27.000Z
- Updated: 2018-08-13T01:05:27.000Z
- Author: Aarthi Ramakrishnan
- Tags: programming, #Import 2026-08-27 15:34

1. [Andrew Ng ML course](https://www.coursera.org/learn/machine-learning?ref=bioinformatics.ghost.io): This course helps one enter the Machine Learning world. It is the ABC's of Machine Learning. Covers the following topics:
- Linear and Logistic Regression
- Neural Networks
- Performance evaluation of ML models
- Support Vector Machines
- Dimensionality Reduction, PCA
- Anomaly Detection
- Recommender Systems
1. [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/?ref=bioinformatics.ghost.io): This book delves deeper into the concepts taught by Andrew Ng in his Coursera course. A must read. Also check out related [course by the authors](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/?ref=bioinformatics.ghost.io)
2. [OCDevel Machine Learning guide podcast](http://ocdevel.com/mlg?ref=bioinformatics.ghost.io): These podcast episodes are really helpful if you want to make use of your commute time. Tyler Renelle has done a great job of explaining the intuition behind each ML algorithm.
3. [Data School scikit-learn videos](https://www.youtube.com/user/dataschool/videos?ref=bioinformatics.ghost.io): Helpful videos to get started with applying your Machine Learning knowledge using scikit-learn.