Unit 1 : Introduction to Machine Learning

* Introduction to Machine Learning
* Python for Machine Learning
* Supervised vs Unsupervised ML
* Applications of Machine Learning
* Advantages of using Python libraries for ML

Unit 2 : Introduction to Deep Learning

* Deep Learning: A revolution in Artificial Intelligence
* Limitations of Machine Learning
* What is Deep Learning?
* Advantages of Deep Learning over Machine learning
* Three Reasons to go for Deep Learning
* Real-Life use cases of Deep Learning
* Review of Machine Learning
* Regression
* Classification
* Clustering
* Reinforcement Learning
* Underfitting and Overfitting
* Optimization

Unit 3 : Understanding Neural Networks with TensorFlow

* How Deep Learning Works?
* Activation Functions
* What is a Perceptron
* Training a Perceptron
* Important Parameters of Perceptron
* What is TensorFlow?
* TensorFlow code-basics
* Graph Visualization
* Constants, Placeholders, Variables
* Creating a Model
* Step by Step – Use-Case Implementation

Unit 4 : Deep dive into Neural Networks with TensorFlow

* Understand limitations of a Single Perceptron
* Understand Neural Networks in Detail
* Illustrate Multi-Layer Perceptron
* Backpropagation – Learning Algorithm
* Understand Backpropagation – Using Neural Network Example
* MLP Digit-Classifier using TensorFlow
* TensorBoard

Unit 5 : Master Deep Networks

* Why Deep Networks
* Why Deep Networks give better accuracy?
* Use-Case Implementation on SONAR dataset
* Understand How Deep Network Works?
* How Backpropagation Works?
* Illustrate Forward pass, Backward pass
* Different variants of Gradient Descent
* Types of Deep Networks

Unit 6 : Understand CNN and RNN

* Introduction to CNNs
* Applying CNNs
* Introduction to RNNs
* Applying RNNs

Unit 7 : Keras API

* Define Keras
* How to compose Models in Keras
* Sequential Composition
* Functional Composition
* Predefined Neural Network Layers
* What is Batch Normalization
* Saving and Loading a model with Keras
* Customizing the Training Process
* Using TensorBoard with Keras
* Use-Case Implementation with Keras

Unit 8 : TFLearn API

* Define TFLearn
* Composing Models in TFLearn
* Sequential Composition
* Functional Composition
* Predefined Neural Network Layers
* What is Batch Normalization
* Saving and Loading a model with TFLearn
* Customizing the Training Process
* Using TensorBoard with TFLearn
* Use-Case Implementation with TFLearn