Phase-1/Session1

Agenda:

The objective of Session 1 will be to get you started with Machine Learning and Deep Learning by going over the key ideas and concepts required to build any Machine Learning or Deep Learning models.In the session we,will cover several Hands on exercises to resonate to our promise Intuition to Implementation as it is rightly said by Joel Spolsky :“Its harder to read code than to write it.”

What we will cover:

1. Machine Learning Basics

   • What are Algorithms

   • Bias vs Variance trade off

   • Underfitting, Overfitting

   • Hyperparameters and Validation Sets

   • Gradient Descents

   • Linear Regression

   • Logistic Regression

2. Deep Learning Basics

   • Neural Networks

   • Backpropagation

   • Activation and Loss Function

   • Optimizers

3. Building Image Classification Model using Keras and Pytorch (MNIST Dataset).