Introduction to Python Programming

Installation Of Anaconda Navigator

Data Types in Python

Operations in Python

Control Structures in Python

Functions

OOPs in Python (Optional)

Python for Data Science

Introduction to NumPy

Operations in NumPy

Indexing NumPy Arrays

Panda Series and DataFrames

Operations

Groupby Functions

Joining, Concatenating and merging DataFrames

Data Visualisation in Python

Data Visualisation Using Matplotlib

Data Visualisation Using Seaborn

Plotly

Exploratory Data Analysis

Web Scraping

Data Wrangling

Data Preparation

Missing Value Imputation

EDA

Introduction to Database Design

Understanding ERD

Various Constraints in SQL

Star Schema

Database Creation and Modification in MySQL workbench

Queries in SQL

Combining Tables

SELECT Statement

Advanced Filtering

SQL Transactions, Variables and Procedures

Descriptive Statistics

Types of Data

Population VS Sample

Central Tendency

Measures of Dispersion

Correlation

Inferential Statistics

Probability and Probability Distribution

Binomial Distribution

Normal Distribution

Z-Score

Standard Normal Distribution

T Distribution

Central Limit Theorem

Hypothesis Testing

Null and Alternative Hypothesis

Significant Level

T-Statistics

P-value

Type I and Type II Error

A/B Testing With Practical Examples

Introduction to Machine Learning

Supervised and Unsupervised learning

Introduction to scikit-learn

Understanding Classification problems, regression problem, dependent and independent variables

Train, Test and Optimize regression and Classification Models

Supervised Learning

Linear Regression

Logistic Regression

Decision Tree

Introduction to NLP and Lexical Processing

Unsupervised Learning: Clustering

Introduction to clustering problems

K-Means Clustering

Other Types of Clustering

Forecasting Algorithms

Introduction to Time series Forecasting

Different Forecasting Models

Introduction to Git and GitHub

Introduction to Version Control System

Git Essentials

Git Commands and Functionalities

Create Your First Project

Random Forests and Bagging

Bagging for Imbalanced Classification

Random Forest for Imbalanced Classification

Easy Ensemble for Imbalanced Classification

Boosting

Adaboost

XG Boost

Gradient Boosting

Model Selection

Principles of Model Selection

Feature Engineering

Model Evaluation

Class Imbalance

Principal Component Analysis

Introduction to PCA

Vector Representation and Operation

Matrix Operation (Recap)

Dimensionality Reduction

Model Performance

Advanced Regression

Estimating Coefficients in SLR and MLR

Matrix representation of SLR and MLR

Assumption of Linear Regression

Identify Non-linear data

Polynomial Regression

Regularisation (Ridge and Lasso)

Introduction to Computer Vision

Image Detection/Handwriting Classifier: OpenCV Demo

Introduction to Neural Network

Convolutional Neural Network

How to Build CNN in Python Keras

Image Augmentation: Cats vs Dogs Classifier

Principles of Object Detection

TensorFlow Object Detection APIs

Dark Flow and YOLO: Object Detection

Style Transfer: Make AI Generated Art

Domain-based Capstone Projects- Learning Outcome

Problem Statement and Project Objectives

Approach for the Solution

Optimum Solutions

Evaluation Metrics

Gathering Actionable insights