Machine Learning and AI Concepts in Python
Kickstart your journey into AI and machine learning with this immersive, hands-on module on Scikit-learn and Pytorch. Explore real-world AI applications in biology.
Overview
This module offers an engaging introduction to artificial intelligence and machine learning, designed to help you quickly build practical, in-demand skills.
In the first week, participants dive into essential machine learning techniques—including regression, classification, clustering, and dimensionality reduction—through hands-on Python exercises using scikit-learn. You’ll work with a wide range of real-world datasets, spanning both biological and non-biological domains, to see how these methods are applied in practice.
In the second week, students expand their perspective on AI, exploring impactful applications in biology and beyond while gaining their first experience with PyTorch.
Prerequisites
Python skills with knowledge of numpy + pandas.
Day-to-day Plan
Day 1 - Introductory Concepts
Introduction to ML and AI, and their applications. Also, you will get basic familiarity with the math of this field without going into a lot of detail.
Day 2 - Data Preparation and Visualization
Introduction to pandas and numpy. Basics of statistical analysis. Data preparation - train/test split.
Day 3 - Scikit-Learn - Regression
Building linear regression models with Scikit-Learn and predicting outcomes
Day 4 - Scikit-Learn - Classification
Logistic regression and k-NN using Scikit-Learn.
Day 5 - Scikit-Learn - Clustering
Clustering - k-means, dimensionality reduction. Unsupervised learning.
Day 6 - Real-life example
Exercises from Kaggle
Day 7-8 - Deep Learning and Pytorch
Introduction to Pytorch. Rebuilding prior exercises.
Day 9-10 - AI in Biology
Application of artificial intelligence in biological data modeling.
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