Course Contents

Introduction

1. Introduction to Machine Learning 

  • Basic Terminology

  • Tools

  • Client Server Architecture and Various Protocol

  • Relational And Non-Relational 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

4. Database Connection

  • Learn how to connect Python Application with database with Basic CRUD Operation

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

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

8. Scientific Calculation Library in Python

  • Learn Scientific calculation and Machine Learning using TensorFlow

  • Learn Scientific calculation and Machine Learning using SciKit-Learn

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, K-mean 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

17. Deep Learning Advance 

  • Convolution neural network theory & examples

  • Image pixel data vs image feature data

  • Recurrent neural network theory, Long short-term 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