• What is AI (Artificial Intelligence) ?
• What types of intelligences we are talking about?
• Different definitions and Ultimate goal of AI.
• What are application areas for AI?
• History of AI and some real life examples of AI.

• What is ML and How it is related with AI?
• What is NLP and How it is related with AI?
• What is DL and How it is related with ML and AI?
• What are ANNs and DNNs and How are they related to AI?

Project 1 – These simple tasks are to make you understand how AI and ML can find their applications in real life.

• What are Libraries, packages and Modules?
• What are top Python libraries for ML in Python?

• Why choosing Anaconda development environment?
• Setting up Anaconda development environment on Windows 10 PC.
• Verifying proper installation of Anaconda environment.

• What is a classifier in ML?
• Important elements and flow of any ML projects.
• Let’s develop our first ML program – explanations
• Let’s develop our first ML program – development

These simple tasks are going to give you some great experience with Machine Learning introductory programs or better say, “Hello world” programs of Machine Learning.

• What all ML techniques are there?
• Evaluation methods of all ML techniques

• Developing complete ML project – understanding data set
• Developing complete ML project – understanding flow of project
• Developing complete ML project – visualizing data set through Python
• Developing complete ML project – development
• Developing complete ML project – concepts explanations
• –(Digit recognition project) Developing another project of ML.

• After completing these project, you have done and understood multiple complete projects of Machine Learning.

• Installation
• CPU Software Requirements
• CPU Installation of PyTorch
• PyTorch with GPU on AWS
• PyTorch with GPU on Linux
• PyTorch with GPU on MacOSX

• Matrix Basics
• Seed for Reproducibility
• Torch to NumPy Bridge
• NumPy to Torch Bridge
• GPU and CPU Toggling
• Basic Mathematical Tensor Operations
• Summary of Matrices

• Variables
• Gradients
• Summary of Variables and Gradients

• Linear Regression Introduction
• Linear Regression in PyTorch
• Linear Regression From CPU to GPU in PyTorch
• Summary of Linear Regression

• Logistic Regression Introduction
• Linear Regression Problems
• Logistic Regression In-depth
• Logistic Regression with PyTorch
• Logistic Regression From CPU to GPU in PyTorch
• Summary of Logistic Regression

• Logistic Regression Transition to Feedforward Neural Network
• Non-linearity
• Feedforward Neural Network in PyTorch
• More Feedforward Neural Network Models in PyTorch
• Feedforward Neural Network From CPU to GPU in PyTorch
• Summary of Feedforward Neural Network

• Feedforward Neural Network Transition to CNN
• One Convolutional Layer, Input Depth of 1
• One Convolutional Layer, Input Depth of 3
• One Convolutional Layer Summary
• Multiple Convolutional Layers Overview
• Pooling Layers
• Padding for Convolutional Layers
• Output Size Calculation
• CNN in PyTorch
• More CNN Models in PyTorch
• CNN Models Summary
• Expanding Model’s Capacity
• CNN From CPU to GPU in PyTorch
• Summary of CNN

• Introduction to RNN
• RNN in PyTorch
• More RNN Models in PyTorch
• RNN From CPU to GPU in PyTorch
• Summary of RNN

• Introduction to LSTMs
• LSTM Equations
• LSTM in PyTorch
• More LSTM Models in PyTorch
• LSTM From CPU to GPU in PyTorch
• Summary of LSTM

Note: Depends upon Trainers above projects may vary