ML Lab Book

Labs

Labs can be downloaded from here: [ Raw .ipynb files.]

Lab1 - Introduction

Lab1A - Python Basics

Lab1A - Python Basics

Lab1B - Numpy Basics

Lab1C - Pandas Basics

Lab1D - Ecommerce Purchases [Solutions]

Lab1E - SF Salaries [Solutions]

Lab1F - Data Analysis [Solutions]

Lab2 - Linear Regression

Lab2A - Linear Regression

Lab2B - Supervised Learning

Lab3 - Logistic Regression and Naive Bayes

Lab3A - Logistic Regression

Lab3B - Naive Bayes

Lab4 - Bayesian Networks

Lab4A - Bayesian Networks

Lab4B - Creating a Bayesian Network

Lab5 - Decision Trees

Lab5A - Decision Trees

Lab5B - Ensemble Methods

Lab5C - Decision Trees [Solution]

Lab5D - Ensemble Methods [Solutions]

Lab6 - Support Vector Machines

Lab6A - Support Vector Machines

Lab6B - Support Vector Machines [Solutions]

Lab7 - Neural Networks I

Lab7A - MNIST

Lab7B - Xor-Net

Lab8 - Neural Networks II

Lab8A - Tensors in PyTorch

Lab8B - Neural Networks in PyTorch

Lab8C - Training Neural Networks

Lab8D - Fashion-MNIST

Lab9 - Clustering

Lab9A - K-Means

Lab9B - Agglomerative Clustering

Lab9C - Agglomerative Clustering [Example]

Lab9D - DBSCAN

Lab9E - Cluster Comparisons

Lab9F - K-Means [Solutions]

Lab9G - Clustering for Classification [Kaggle]

Bonus : Interactive K-means Demo.