Introduction

Content of this course

What is a vector, its properties and what are unit vectors

Linear combination of vectors (independent and dependent)

What is a matrix

Transpose of a matrix

Determinant of a matrix

Adjoint of a matrix

Eigenvectors and Eigenvalues of a matrix

Diagonal of a matrix

Introduction

Review on Precalculus

Limits and Continuity

Differentiation

Derivatives of Transcendental Functions

Limits - Indeterminate Forms

Applications: Rates of Change

Applications: Differential Equations

Applications: Mean Value Theorem

Applications: Linear Approximation

Applications: Curve Sketching

Applications: Optimization Problems

Conclusion

Unveiling Uncertainty: Exploring Probability Basics and Random Experiments

Unlocking the World of Combinations and Permutations in Combinatorics

Data Analysis with Google Colab: Central Tendency & Probability

Simulating Randomness: Sampling from Distributions

Get Your Data Ready to Shine: Data Cleaning and Preprocessing Fundamentals

Introduction to the course

Introduction to Python Programming

Variables, Comments, Type Casting, Strings, Taking Input from User

Python Operators, Data Types and Control Structures

Arrays, Command Line Arguments, Functions

Classes and objects, Inheritance and Overriding, Abstract Classes and Interfaces

Working with Date and Time and File Handling

Working with Exception Handling and Regular Expressions

Introduction to the Course

R & R Studio Set up

Mastering the Basics of R

Data Structures in R

Vectors in R

Matrices in R

Arrays in R

Lists in R

Data Frames in R

Working with Categorical Data in R

Functions in R

if-else statements in R

Coding Loops in R

Data Analysis: The apply family of functions

Importing Data into R with tidyverse package

Data Analysis, Transformation & Transformation

Merging with merge function

Restructuring Datasets with reshape package

Restructuring Datasets with tidyr package

Data Aggregation using R

dplyr package

data.table package

MySQL Installation

Introduction to SQL & Creating Databases

Functions & Like Operator

Aggregate functions with Group By & Having'

Joins & Union

Case Expressions , CTE's, Subqueries and Temporary Tables

Views, Indexing & Normalisation

Window Functions & Triggers

Transactions and Locking

Large Databases and clustering

Introduction to NoSQL: MongoDB

Introduction to Reliability

Quiz

Getting started with the journey

Tableau Fundamentals Crash Course

Calculations: QTC and Level of Detail Expressions

Sales Analysis Dashboard

Netflix Analysis Dashboard

Investment Portfolio

Top 10 books

Introduction

Connecting To Data Sources

Data Processing With Tableau

Data Visualization And Reporting With Tableau

Welcome to the course

The Fundamentals

The Foundations of Big Data

Environment and Installation

Hadoop Ecosystem

Hadoop Distributed File system

Map Reduce

HIVE

PYTHON FOR PYSPARK

APACHE SPARK

OVERVIEW OF SPARK

STRUCTURED API OVERVIEW

OPERATIONS ON DATAFRAMES

WORKING WITH DIFFERENT TYPES OF DATABASE

CREATING DATAFRAMES FROM DIFFERENT SOURCES

AGGREGATIONS

SPARK JOINS

RESILIENT DISTRIBUTED DATASETS- RDDs

DISTRIBUTED VARIABLES

HOW SPARK RUNS ON A CLUSTER

Introduction

Apache Cassandra Installation

Cassandra Query Language (CQL)

Apache Cassandra Architecture

Integration of Apache Spark with Cassandra

Introduction

Overview

Basic Mongo Operations

Basic Create Operation

Basic Update Operation

Basic Read Operation

Basic Delete Operation

Query and projection operators

Update Operators

Mongo with Node

Mongo with Python

Django with Mongo

Spark With Mongo

Python Quick Refresher (Optional)

Essential Python Libraries for Data Science

Fundamental NumPy Properties

Mathematics for Data Science

Python Pandas DataFrames & Series

Data Cleaning

Data Visualization using Python

Exploratory Data Analysis

Time Series in Python

Machine Learning With R

Quiz Section

Introduction

NumPy

NumPy Array

Indexing Arrays of Arrays

Matlplotlib

Pandas

Scikit Learn

Learning and Predicting

Cross Validation

Movie Review Analysis

Introduction

A Bit of Theory

Installation and Setup

Say Hi to Keras

Real World Case Study: Predicting Protein Functions

Convolutional Neural Networks (CNN)

Graph-based Models

Finishing Touches

Introduction

Unsupervised Learning - Clustering

Dimensionality Reduction

Association Rule & Market Basket Analysis

Introduction

Course Content

Python Primer: A Beginner's Journey into Python's Fundamentals

Machine Learning Foundations: A Beginner's Guide to the Basics of ML

Deep Learning Demystified: An Introduction to the Basics of Deep Learning

Time Series Insights: An Introduction to the Basics of Time Series Analysis

Flight Fare Prediction Project-1: Predicting and Analyzing Flight Ticket Prices

Mushroom Classification Project-2: Exploratory Data Analysis for Insightful

Nursery School Application Classification Project-3: Regression Analysis

Toxic Comments Classification Project-4 : Identifying and Analyzing Toxic

UK Road Accident Timeseries Analysis: Exploratory Data Analysis for Forecasting

Introduction

Types of Data

Transformation and Model

Introduction

REGRESSION CASE STUDY : Retail Store Sales Prediction

CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge

REGRESSION CASE STUDY : Restaurant Sales Prediction

CLASSIFICATION CASE STUDY : Credit Card Fraud Detection

REGRESSION CASE STUDY : Inventory Prediction

CLASSIFICATION CASE STUDY : Diabetes Prediction

REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing

CLASSIFICATION CASE STUDY : Breast Cancer Prediction

REGRESSION CASE STUDY : Coal Production Estimation

CLASSIFICATION CASE STUDY : Heart Diseases Prediction

CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not

REGRESSION CASE STUDY : Player Salary Prediction

Conclusion

Introduction to Course

Applications of RNN (Motivation)

DNN Overview

RNN Architecture

RNN implementation

Sentiment Classification using RNN

Gradient Descent in RNN

Vanishing Gradients

TensorFlow

Book Writer

Stock Price Prediction

Further Readings and Recourses

Introduction

Image Processing

Object Detection

Deep Neural Network Overview

Deep Neural Network Architecture

Gradient Descent in CNNs

Introduction to TensorFlow

Classical CNNs

Transfer Learning

Yolo

Face Verification

Neural Style Transfer

Deep Learning

Introduction to Course and Instructor

Introduction to Images

2D Scaling Transformations

2D Geometric Transformations

Geometric Transformation Estimation(Panorama)

Binary Morphology

Image Filtering

Canny Edge Detector

Shape Detection

Shape Detection Hough Transform

Corner Detection

Automatic Panorama SIFT

Object Detection

YOLO Object Detector

Motion

Object Tracking

3D Reconstruction

Smart CCTV Project

Introduction

Python[Optional]

Python's Essential Libraries[Optional]

Computer Vision

Machine Learning

Machine Learning Hands-On

Collision Avoidance

Deep Learning

Deep Learning: Hands-On

Control Theory

Introduction

Introduction(Regular Expressions)

Meta Characters(Regular Expressions)

Pattern Objects(Regular Expressions)

More Meta Characters(Regular Expressions)

String Modification(Regular Expressions)

Words and Tokens(Text Preprocessing)

Sentiment Classification(Text Preprocessing)

Language Independent Tokenization(Text Preprocessing)

Text Nomalization(Text Preprocessing)

String Matching and Spelling Correction(Text Preprocessing)

Language Modeling

Topic Modelling with Word and Document Representations

Word Embeddings LSI

Word Semantics

Word2vec(Optional)

Need of Deep Learning for NLP(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning DNN)

Training(NLP with Deep Learning DNN)

Hyper parameters(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning RNN)

Mini-project Language Modelling(NLP with Deep Learning RNN)

Mini-project Sentiment Classification(NLP with Deep Learning RNN)

RNN in PyTorch(NLP with Deep Learning RNN)

Advanced RNN models(NLP with Deep Learning RNN)

Neural Machine Translation

Six Sigma and the organizations

Define Phase

Measure phase

Analyze phase

Improve Phase

Control Phase

Introduction

Generative Artificial Intelligence

Navigate ChatGPT

Conclusion

Welcome

Getting Started

Data Exploration - What can you tell me about the data?

Enough Python to start your journey

My journey to data wrangling

Structured data - Tables

More on Python - Functions, properties and some other goodies

Unstructured Data

Web services or Application Programming interfaces

Lists, dictionaries and data types in Python

How to make data useful - Structuring

How do I clean data?

Enrichment - how to make data valuable

Validation - making sure the data is correct

Publishing - querying, sharing and presenting data

Conclusion

Computer Vision

Data Science with Python

Machine Learning

Deep Learning Fundamentals

Deep Learning with Tensorflow and Keras

Data Visualization and Tools

Natural Language Processing (NLP)