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