1: Introduction to Artificial Intelligence

·   Decoding Artificial Intelligence

·   Fundamentals of Machine Learning and Deep Learning

·   Machine Learning Workflow

·   Performance Metrics

 2: Statistics Essential

·   Introduction

·   Sample or population data?

·   The fundamentals of descriptive statistics

·   Measures of central tendency, asymmetry, and variability

·   Practical example: descriptive statistics

·   Distributions

·   Estimators and estimates

·   Confidence intervals: advanced topics

·   Practical example: inferential statistics

·   Hypothesis testing: Introduction

·   Hypothesis testing: Let’s start testing!

·   Practical example: hypothesis testing

·   The fundamentals of regression analysis

·   Subtleties of regression analysis

·   Assumptions for linear regression analysis

·   Dealing with categorical data

· Practical example: regression analysis

3: Python for Data Science

·   Python Basics

·   Python Data Structures

·   Python Programming Fundamentals

·   Working with Data in Python

·   Working with NumPy arrays

4: Data Science with Python

·   Data Science Overview

·   Data Analytics Overview

·   Statistical Analysis and Business Applications

·   Python Environment Setup and Essentials

·   Mathematical Computing with Python (NumPy)

-   Scientific computing with Python (Scipy)

-   Data Manipulation with Pandas

-   Machine Learning with Scikit–Learn

-   Natural Language Processing with Scikit Learn

-   Data Visualization in Python using matplotlib

-   Web Scraping with Beautiful Soup

-  Python integration with Hadoop MapReduce and Spark

5: Machine Learning

·   Introduction to Artificial Intelligence and Machine Learning

·   Data Preprocessing

·   Supervised Learning

·   Feature Engineering

·   Supervised Learning-Classification

·   Unsupervised learning

·   Time Series Modelling

·   Ensemble Learning

·   Recommender Systems

·   Text Mining

6: Deep Learning with Tensor Flow

·   Introduction to Tensor Flow

·   Convolutional Neural Networks (CNN)

·   Recurrent Neural Networks (RNN)

·   Unsupervised Learning

·   Autoencoders

7: Advanced Deep Learning and Computer Vision

·   RBM and DBNs

·   Object Detection Using Convolutional Neural Net

·   Variational AutoEncoder

·   Generating Images with Neural Style

·   Working with Deep Generative Models

·   Distributed & Parallel Computing for Deep Learning Models

·   Reinforcement Learning

·   Deploying Deep Learning Models and Beyond

8: Natural Language Processing

·   Introduction to Natural Language Processing

·   Feature Engineering on Text Data

·   Natural Language Understanding Techniques

·   Natural Language Generation

·   Natural Language Processing Libraries

·   Natural Language Processing with Machine Learning and Deep Learning

·   Speech Recognition Technique

9: AI Capstone Project