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This course is designed for absolute beginners in Machine Learning with no previous knowledge in ML techniques.

Lecture 1. Machine Learning Intuition

The intention here is to provide a basic intuition of Machine Learning (ML) that is applicable to many problems. This notebook is fully written in basic python, no higher level packages have been used.

Jupyter Notebook

Lecture 2. Linear Regression with numpy

In this lecture, we introduce the importance of vectorization and provide the vectorized implementation for the Linear model. Also, provide an overview of broadcasting.

Jupyter Notebook

Lecture 3. Classification - Logistic Regression

Classification is a bread and butter task in Machine Learning. Given a few categories, we train a model using a dataset, that predicts the category/class of the new data point.

As usual, we will define a toy dataset and build out the complete logistic classification model from scratch in python. We will leverage cross entropy loss function to train the model.

Jupyter Notebook