An Update on The Guide for Biologists on Quickly Mastering Machine Learning and AI Concepts
In late September, I started posting a set of tutorials to help biologists (with some Python skills) and bioinformaticians quickly master machine learning and AI concepts. Here are the earlier posts -
A Guide for Biologists on Quickly Mastering Machine Learning and AI Concepts - (i)
A Guide for Biologists on Quickly Mastering Machine Learning and AI Concepts - (ii)
A Guide for Biologists on Quickly Mastering Machine Learning and AI Concepts - (iii a)
Based on the traffic, I decided to develop a full series (with text and video lessons) on the topic. This series is also an experiment on a new way of learning, and I will explain that in a later post.
Here is my full plan for the series. It will include 12 learning lessons and 2 exploratory bonus lessons on cutting-edge topics. I will try to connect each lesson with its relevance for biology.
- 01: Intro to ML with Scikit-learn
- 02: Introduction to AI with Pytorch
- 03: Pytorch Code Explained
- 04: A Journey through the Concepts
- 05: Analyzing Iris Dataset using Scikit-learn
- 06: Analyzing Iris Dataset using Pytorch
- 07: Supervised Learning Techniques using Scikit-learn
- 08: Clustering Methods with Scikit-learn
- 09: Dimensionality Reduction (PCA, tSNE) with Scikit-learn
- 10: Neural Network Concepts
- 11: Iris Revisited
- 12: Building a Handwritten Digit Classifier with PyTorch
- Bonus Lesson - How Does ChatGPT Work?
- Bonus Lesson - How Do Protein Folding Programs like Alphafold and RoseTTAfold Work?
As the lessons develop, I will post them partly or fully here on the blog. If you like to receive early information, please join here.
