Course Introduction

1

Introduction of Machine Learning and its application in Day to Day life

2

Course overview and Dashboard description

Python Core

1

Introduction to python and comparison with other programming language

2

Installation of Anaconda Distribution and other python IDE

3

Python Objects, Numbers & Booleans, Strings, Container objects, Mutability of objects

4

Operators -Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and associativity

5

Conditions(If else, if-elif)

6

Loops(While ,For)

7

Break and Continue statements

8

Range functions

String Objects and Collections

1

String Object Basics

2

String Methods

3

Splitting and Joining Strings

4

String format functions

5

List Object Basics

6

List Methods

7

List as Stack and Queues

8

List Comprehensions

Tuples, Set, Dictionaries & Functions

1

Tuples, Sets, Dictionary Object basics, Dictionary Object methods, Dictionary View Objects

2

Functions Basics, Parameter passing, Iterators

3

Generator functions

4

Lambda functions

5

Map, Reduce & filter functions

OOPS Concepts &Working with Files

1

OOPS Basic Concepts

2

Creating Classes and Objects

3

Inheritance & Multiple Inheritance

4

Working with files

5

Reading and Writing files

6

Buflered read and BuPered write

7

Other File methods

Modules ,Exception Handling & Database Programming

1

Using Standard Module

2

Creating new modules

3

Exceptions Handling with Try-except

4

Creating ,Inserting and Retrieving Table

5

Updating and Deleting the data

Visualization

1

Matplotlib

2

Seaborn

3

Plotly

4

Cuflinks

Rest API

1

Flask Introduction

2

Flask Application

3

Open link Flask

4

App Routing Flask

5

URL Building Flask

6

HTTP Methods Flask

7

Templates Flask

8

Django end to end

Database

1

Mongo DB

2

SQL lite

3

Python SQL

Python project

1

Web crawlers for image data sentiment analysis and product review sentiment analysis

2

Integration with web portal

3

Integration with rest API ,web portal and mongo db on Azure

4

Deployment on web portal on Azure

5

Text mining

6

Social media data churn

Exploratory Data Analysis

1

Feature Engineering and Feature Selection

2

Building Tuning and Deploying Models

3

Analyzing Bike Sharing Trends

4

Analyzing Movie Reviews Sentiment

5

Customer Segmentation and Elective Cross Selling

6

Analyzing Wine Types and Quality

7

Analyzing Music Trends and Recommendations

8

Forecasting Stock and Commodity Prices

Machine Learning -1

1

Introduction

2

Supervised, Unsupervised, Semi-supervised & Reinforcement

3

Train, Test & Validation Split

4

Performance

5

Over fitting & Under fitting

6

OLS

7

Linear Regression

8

Assumptions

9

R-square & adjusted R-square

10

Intro to Scikit learn

11

Training Methodology

12

Hands on Linear Regression

13

Ridge Regression

          14

Logistics Regression

15

Precision Recall

16

ROC-curve

17

F-Score

Machine Learning -2

1

Decision Tree

2

Cross Validation

3

Bias vs Variance

4

Ensemble Approach

5

Bagging & Boosting

6

Random Forest

7

Variable Importance

Machine Learning -3

1

XG Boost

2

Hands on XG Boost

3

K Nearest Neighbor

4

Lazy learners

5

Curse of Dimensionality

6

KNN Issues

7

Hierarchical Clustering

8

K-Means

9

Performance Measurement

10

Principal Component Analysis

11

Dimensionality Reduction

12

Factor Analysis

Machine Learning Project

1

Healthcare analytics prediction of medicines based on FIT BIT Band

2

Revenue forecasting for startups

3

Prediction of order cancellation at the time of ordering inventories

4

Anamoly detection in inventory packaged material

5

Fault detection in wafers based on sensor data

6

Demand forecasting for FMCG product

7

Threat identification in security system

8

Defect detection in vehicle engine

9

Food price forecasting with zomato dataset

Deployment

1

Deployment of all the project In cloud foundry , AWS ,AZURE and google cloud platform

2

Expose API to web browser and mobile application

3

Retraining approach of Machine learning model

4

Devops infrastructure for machine learning model

5

Database integration and scheduling of machine learning model and retraining

6

Custom machine learning training approach

7

AUTO ML

8

Discussion on infra cost and Data volume

9

Prediction based on Streaming data