**Module – 1**

**Data**

– Tabular data

– Data exploration with Pandas code along

– Visual data Exploration

– Plotting with Matplotlib

– Unstructured Data

– Images and Sound in Jupyter

– Exercises

** Machine Learning**

– Machine Learning Problems

– Supervised Learning

– Linear Regression

**Module – 2**

**Machine Learning**

– Cost Function

– Cost Function code along

– Finding the best model

– Linear Regression code along

– Evaluating Performance

– Evaluating Performance code along

– Classification

– Classification code along

– Overfitting

– Cross Validation

– Cross Validation code along

– Confusion matrix

– Confusion Matrix code along

– Feature Preprocessing code along

**Module – 3**

**Deep Learning Intro**

– Deep Learning successes

– Neural Networks

– Deeper Networks

– Neural Networks code along

– Multiple Outputs

– Multiclass classification code along

– Activation Functions

** Gradient Descent**

– Derivatives and Gradient

– Back-propagation intuition

– Chain Rule

– Derivative Calculation

– Fully Connected Backpropagation

– Matrix Notation

– Numpy Arrays code along

– Learning Rate

– Learning Rate code along

– Gradient Descent

– Gradient Descent code along

– EWMA

– Optimizers

**Module – 4**

**Convolutional Neural Networks**

– Features from Pixels

– MNIST Classification

– MNIST Classification code along

– Beyond Pixels

– Images as Tensors

– Tensor Math code along

– Convolution in 1 D

– Convolution in 1 D code along

– Convolution in 2 D

– Image Filters code along

– Convolutional Layers

– Convolutional Layers code along

– Pooling Layers

– Pooling Layers code along

– Convolutional Neural Networks

– Convolutional Neural Networks code along

– Weights in CNNs

– Beyond Images

**Module – 5**

**Recurrent Neural Networks**

– Time Series

– Sequence problems

– Vanilla RNN

– LSTM and GRU

– Time Series Forecasting code along

– Time Series Forecasting with LSTM code along

– Rolling Windows

– Rolling Windows code along

**Case Studies**