Course Contents
Introduction
1. Introduction to Machine Learning

Basic Terminology

Tools

Client Server Architecture and Various Protocol

Relational And NonRelational Database
Basics
2. Python Basics:

Introduction to programming in Python

Learn how Jupyter Notebooks work

Learn data structures, data operations

Learn if else statements, for and while loops, and logical operations.
Databases
3. Database

Learn MySql

Learn MongoDB

Learn OrientDB
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4. Database Connection

Learn how to connect Python Application with database with Basic CRUD Operation
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5. Database Operations

User Authentication

Tabular and graphical representation of data

Various type of Data (Organised and Unorganised Text, Data Stream, Image, Audio and Video) and Information,

Sorting and Searching in Python
Advanced
6. Python Advanced

Learn advanced functionality in Python including functions

Learn debugging, error handling

Learn string manipulations and writing efficient code

File and Folder Operation
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7. Data Manipulation and Visualization Library in Python

Learn Data manipulation and Visualization using NumPy

Learn Data manipulation and Visualization using SciPy

Learn Data manipulation and Visualization using Pandas

Learn Data manipulation and Visualization using Matplotlib
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8. Scientific Calculation Library in Python

Learn Scientific calculation and Machine Learning using TensorFlow

Learn Scientific calculation and Machine Learning using SciKitLearn
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9. Natural Language Processing and Statistics Library in Python

Learn Natural Language Processing and Statistics using NLTK,

Learn Natural Language Processing and Statistics using Scrapy

Learn Natural Language Processing and Statistics using Statsmodels
Data Science
10. Learn Data Science

Introduction to Data Science

Introduction to Basic Statistics
Machine Learning
11. Machine Learning Overview

What is machine learning?

Why to go for machine learning?

Application of machine learning in several areas,

Sample Project Assignment to be developed as part of Course and build testing models based on Machine Lear
12. Linear Algebra and Basic Statistics

Learn Matrix and vector operations

Learn Systems of linear equations and solutions

Learn Matrix factorization and transformations

Learn Eigen analysis of the data

Learn Principal Component Analysis (PCA)

Learn Basic probability, expected value and variance

Learn Implementation using Python and Tensor Flow
13. Optimisation

Learn Quadratic Energy function

Learn Gradient Descent, Stochastic Gradient Descent

Learn Regularization, Sparse representation
14. Regression

Learn Linear Regression

Learn Prediction based on Regression models

Develop Regression model for Testing activities using learning made so far
15. Classification

Overview (binary and multi class), Support vector machine, Kmean clustering.

Logistic regression, Decision Trees and random forest,

Examples and comparison between several classification methods.

Develop Classification model for Testing activities using learning made so far
Deep Learning
16. Deep Learning Overview

Neural network basics

NN structure

Activation functions

Forward and backward propagation
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17. Deep Learning Advance

Convolution neural network theory & examples

Image pixel data vs image feature data

Recurrent neural network theory, Long shortterm memory with examples

Develop Deep Learning model for Test activities using learning made so far
Other materials provided along with the Machine Learning in testing training

PDF files, PPT files as walk through during classroom

Life time access to training recorded videos covering Complete Module and Framework development

Sample code as developed during classroom

Support for overseas placements in association with Global Next Generation Automation