Course Content

Introduction to Data Analytics, Data Science, AI, ML Roadmap

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What is Data Analytics, DS, AI/ML

02:01:30

Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML

01:26:51

Induction Classes

0/6

What is Data, Importance of Data

19:22

What are Mathematical Equations? Why ME?

11:31

Fourier Transformation & Fourier Series

10:17

What is Probability and Why Probability

15:57

What is Geometry, What is Linear Algebra, Eigen Values and Eigen Vectors

19:04

What is Calculus

07:58

Mastering Python[Zero to Hero]

0/30

Introduction to Python

01:02:54

Why Python, Value, Variable, Function, Library[Roadmap on Python]

01:04:10

IDE in Python, Different Data Types

01:01:53

List, Tuple, Set & Dictionary Overview

57:16

Different List Methods

55:48

Different Tuple Methods

01:04:52

Set & Frozenset

01:00:50

Dictionary & String Manipulations

50:56

Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda

01:03:15

Types of Operators, Conditional Statements

23:42

While Loop, List Comprehension, Break, Continue, Arguments

01:01:18

Functions, Escape Sequences, Lambda Functions

Instance Variable, Class Variable, Class Method

56:53

Association vs Composition & Aggregation

50:02

Oops Concept

36:29

Encapsulation, Inheritance

01:07:07

Polymorphism, Method OverLoading, Method Overriding

53:59

Introduction to Pandas

01:05:22

Data Analysis using Pandas

01:34:29

Introduction to Numpy

57:26

Different Numpy Commands

01:03:00

Introduction to Data Visualisation

51:41

Data Visualisation using Matplotlib

01:00:20

Data Visualization using Seaborn

59:34

Data Visualization using Plotly

41:44

Why Data Cleaning?

47:05

Data Cleaning with Sklearn & Pandas

49:57

Regular Expression Basics

54:01

Applied Statistics

0/40

What is Data? Various Types of Data

Levels of Measurement

Categorical Variables & Visualisation Techniques

Continuous Variables and Visualisations

Measures of Central Tendency

Measuring Skewness

Measures of Dispersion

Covariance & Correlation Coefficient

Box & Whisker Plot

Outlier Identification

Treating Outliers

Sampling Techniques

Statistical Analysis Consideration

Types of Analytics

Sample vs Population

Introduction to Inferential Statistics

Law of Large Numbers

Central Limit Theorem

Probability Distributions

Conditional Probability

Continuous Probability Distribution & Discrete Probability Distribution

PMF, CDF, PDF, PPF in Probability

Binomial Distribution

Poisson Distribution

Normal Distribution

Exponential Distribution

Laplace Distribution

Log-Normal Distribution

Standard Error

Confidence Interval Calculations

Z-Table for Confidence Interval

T-Table for Confidence Interval

Hypothesis Testing

Null & Alternate Hypothesis

Type-1 Error & Type-2 Error

Project Demonstration using all the Statistical Techniques

Mastering Excel

0/22

Introduction to Excel

Basics of Excel Commands

Conditional Formatting in Excel

Different Calculations in Excel

Different Charts in Excel

Pivot Tables in Excel

Conditional Statements in Excel

SumIF & Count IF

Data Analysis Tool Kit

End-to-End Dashboard using Excel

Power Query in Excel

Statistics with Real-Time Project Demonstration on EDA

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Basics of Statistics

01:41:48

Central Limit Theorem, Normal Distribution, Skewness

01:29:44

Real-Time Project Demonstration on EDA with Real-World Project

01:01:55

End-to-End Statistics using Excel

03:31:41

Real-Time Project Demonstration on Probability Distribution, Hypothesis Testing

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Probability Distribution, Random Variable, Binomial, Poisson, Exponential

Normal Distribution

Real-Time Project Demonstration on Probability

Hypothesis Testing Part-1

Hypothesis Testing Part-2

How to Calculate P-Value? Coding Hypothesis Testing

How to solve PDF, CDF, PMF?

Chebyshevs, Log, Power Law, Q-Q, CLT

Real-Time Project Demonstration on Data Analysis Using Excel

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Real-Time Project Demonstration on Data Analysis using Excel

Real-Time Project Demonstration on Data Analysis Using Power BI & MYSQL

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End-to-End Data Analysis using Power BI

Data Analysis using MySQL

Data Analysis using MySQL & DAX Calculations Power BI

How to Prepare for Data Analytics Interviews

Real-Time Project Demonstration on Data Analysis Using Tableau

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End-to-End Data Analysis using Tableau

Mastering MySQL

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Introduction to MySQL

23:29

Creating Database in MySql

19:23

Deep Dive into MySQL

15:42

SELECT, UPDATE, DELETE Operations

29:03

Clauses in MySql Part-1

18:39

Clauses in MySql Part-2

23:31

Clauses in MySQL Part-3

31:31

MySQL Data Handling Part-1

41:21

MySQL Data Handling Part-2

41:22

Advanced MYSQL

0/15

Data Integrity & Referential Integrity

Data Normalisation

First & Second Normal Form

Functional Dependency, Transitive Dependency & 3rd Normal Form

Boyce-Codd Normal Form

Denormalization

Temporary Table, Common Table Expression, Recursive CTE

When to use Temporary Table, CTE & Recursive CTE

Create Events in MySQL

Different Functions in MySQL

Mastering Tableau[Zero to Hero]

0/11

Introduction to Tableau

01:14:02

Basics of Tableau

32:02

Different Types of Charts

30:01

Table Calculations & Parameters

21:51

Waterfall, Pareto Chart, Filters in Tableau

25:12

LOD Expression & Pareto Chart

16:08

MySQL Connection with Tableau

12:10

End-to-End Project using Tableau

19:25

Connect Tableau Desktop with Server

11:35

Tableau Server, Scheduling & Alerts

04:54

Tableau Interview Preparation

07:34

Mastering Power BI[Zero to Hero]

0/27

Introduction to Power Bi

Creating Ribbon Chart

02:37

Creating Scatter Plot

04:10

Creating WaterFall Charts

04:41

Creating Funnel Chart

01:59

Creating Line Plot & Area Plot

05:12

Creating Matrix & Conditional Formatting

06:09

Creating Decomposition Tree

Creating Animated Bar Plot

02:37

Creating Sunburst Chart

02:34

Different Filers in Power BI

04:07

Include & Exclude Operations in Power BI

02:37

Introduction to Power Query

06:11

Groupby & Replace in Power Query

01:52

Merge and Append Operations

02:36

Prefix, Suffix, Length

02:43

Pivot in Power Query

03:51

Introduction to DAX Expressions

03:05

Creating DAX Measures

Publishing Power BI Visualizations

06:39

Unsupervised Learning with Real-Time Projects

0/9

Supervised vs Unsupervised Learning

21:51

Fundamentals of Machine Learning Part-1

32:19

Fundamentals of Machine Learning Part-2

48:41

Insights from Train & Test Accuracy

21:23

Associate Rules Part-1

23:02

Associate Rules Part-2

02:27

Problem Identification & Approach Designing

01:02:13

Associate Rules Scripting

57:15

Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE

01:03:36

Feature engineering Techniques

0/28

Feature Scaling Techniques

Label Encoding Technique

One-Hot Encoding Technique

Dummies Technique

Ordinal Encoding Technique

Imputation Technique

Feature Selection Techniques

Filter Method: Feature Importance

Filter Method: Fishers Score Technique

Filter Method: Chi-Square Technique

Filter Method: Variance Threshold

Filter Method: Correlation Coefficient

Filter Method: Mutual Information

Wrapper Method: Forward Selection Technique

Wrapper Method: Backward Selection Technique

Wrapper Method: Recursive Feature Elimination Technique

Wrapper Method: Exhaustive Feature Selection Technique

Wrapper Method: Sequential Feature Selection Technique

Embedded Methods: Regularisation

Embedded Method: Elastic Net Regression

Embedded Method: Decision Tree

Outlier Treatment

Over Sampling Technique

Under Sampling Techniques

Supervised Learning Algorithms

0/34

How to Prepare Machine Learning Math for Interviews

06:52

Why to use Linear Regression?

06:13

What is Linear Regression

04:42

Case Study on Linear Regression

06:21

Math Behind Linear Regression Part-1

07:03

Math Behind Linear Regression Part-2

06:25

Math behind OLS Technique

04:32

Assumptions of Linear Regression

23:08

Evaluation Metrics for Regression Model

15:50

Accuracy Improving Techniques

14:29

Coding Linear Regression Model

Regularisation Techniques

08:56

Why Logistic Regression

04:27

Math Behind Logistic Regression

17:44

Evaluation Metrics Behind Classification Algorithms

17:10

ROC & AUC Curve

10:20

Coding Logistic Regression Model

Introduction to Decision Tree

06:12

Intuition Behind Decision Tree

07:32

Math Behind Decision Tree

16:03

Math Behind Decision Tree using GINI

04:30

Drawbacks of Decision Tree

05:07

Random Forest and Gradient Boosting

13:08

Coding Decision Tree, Random Forest, GB

SMOTE Technique to Handle Imbalanced Dataset

06:35

Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques

08:47

Coding Feature Selection Techniques

Probability Distributions, Hypothesis Testing

0/8

Probability Distribution, Random Variable, Binomial, Poisson, Exponential

13:07

Normal Distribution

23:00

Real-Time Project Demonstration on Probability

01:34:20

Hypothesis Testing Part-1

13:07

Hypothesis Testing Part-2

05:58

How to Calculate P-Value? Coding Hypothesis Testing

01:15:23

How to solve PDF, CDF, PMF?

10:03

Chebyshevs, Log, Power Law, Q-Q, CLT

22:14

Mastering NLP, NLU, NLG

0/6

Introduction to NLP, NLP Applications

09:12

NLP Challenges

11:59

NLP Pre-Processing Techniques

14:18

NLP Feature Extraction Techniques

10:19

NLP Practical Example

15:48

NLU, Word2Vec, RNN, NER

02:39:52

Pyspark: BigData Processing

0/15

Introduction to Spark

Various divisions in Spark

Grouping, Aggregating and Joining with Spark

Introduction to Machine Learning with PySpark

Machine Learning Pipeline with Spark

Frequent Pattern Mining and Statistics

Feature Transformation with Spark

Data Cleaning & Pre-Processing with Spark

NLP with Pyspark

Spark Structured Streaming

Streaming ML Solutions

MongoDB Mastering

0/10

Introduction to Mongo DB

Installation of MongoDB

Basics Operations of MongoDB

Create Operations in MongoDB

Update Operations in MongoDB

Read Operations in MongoDB

Delete Operations in MongoDB

Query & Projection Operations

Python with MongoDB

Spark with MongoDB

Real-Time Projects to Work as Part of Job simulation

0/11

Data Analytics Real-Time Project-1

Data Analytics Real-Time Project-2

Data Analytics Real-Time Project-3

Data Analytics Real-Time Project-4

Data Science Real-Time Project-1

Data Science Real-Time Project-2

Data Science Real-Time Project-3

Data Science Real-Time Project-4

Real-Time NLP Project-1

Real-Time NLP Project-2

Real-Time NLP Project-3

ML Deployment & MLOps

0/14

End-to-End Deployment Using MLOps & Streamlit

03:11:38

Setup Virtual Environment

04:49

Docker Installation

03:47

How to Activate Virtual Environment

03:03

Docker Desktop

02:51

Regression Model using Pycaret

13:26

Regression Model using Pycaret Part-2

02:10

Interpretability of Model using SHAP

04:46

What is SHAP

01:16

Application Development using Gradio

03:31

Creating API using FASTAPI

03:54

Creating Docker Image

05:29

Model Versioning using Pycaret & MLFlow

06:51

End-to-End ML Proof of Concept

00:00

Advanced Time Series with Real-Time Projects

0/7

Time Series

12:50

Introduction to Time Series Modelling

20:43

Time Series Modelling Part-2

50:18

Characteristics of Time Series Modelling

47:56

Time Series Modelling Techniques

47:05

Simple Time Series Project End-to-End

01:13:04

End-to-End Project on Time Series Modelling

01:06:10

Azure Machine Learning Engineering

0/10

Introduction to Azure Environment

Training & Tuning ML Models using Azure

Working with Data in Azure

Training ML Models in Azure ML

Tuning ML Models in Azure

Azure Automated ML

Deploying & Explaining ML Models

Deploying ML Model for Real-Time Inferencing

Deploying ML Models for Batch Scoring

Responsible AI

AWS Sagemaker Studio

0/2

Setting up AWS Sagemaker & Deploying Simple ML Model

15:13

AIOps with AWS Sagemaker Studio

01:02:44

Hadoop

0/4

Introduction to Hadoop

Understanding HDFS

Using HDFS Command Line

Using HDFS Web Interface

Hadoop MapReduce

0/5

Introduction to MapReduce

MapReduce Platform

Parallel MapReduce

MapReduce Examples

MapReduce YARN Examples

HIVE Database

0/3

Introduction to HIVE

Working on HIVE “SQL” commands

Various Examples on Hive

Deep Learning, Tensorflow & keras

0/7

Introduction to Neural Networks

What are Optimisation Techniques

Math Behind Feed Forward Neural Networks

Back Propagation Techniques

Why Activation Functions

Regression using Tensorflow

Feed Forward Classification using Keras

Computer Vision Mastering Using Tensorflow

0/9

Introduction to Object Detection vs Object Classification

Introduction to CNN

Convolution Matrix

Max Pooling Layer

Point-Wise & Element-Wise Convolution

Batch Normalisation

Strides & Padding

Convolution Filters

End-to-End Computer Vision

Transfer Learning OpenCV & YOLO

0/5

Introduction to Transfer Learning

Headless & With Head Transfer Learning

Object Detection using YOLO

Object Detection using Open CV

Transfer Learning with Tensorflow Hub

R Programming

0/10

Installation of R Studio

19:42

Basics of R Studio

39:40

Data Visualisation with R Studio

33:25

Data Modelling with R Studio

13:53

Data Joining with R Studio

09:45

Data Visualisation with GGPlot2

11:03

Data Preparation with R Studio

09:39

Data Cleaning with Regexp

34:30

Data Visualisation with Plotly

44:40

test

Interview Questions: Frequently Asked Statistics

0/10

Introduction to Statistics

Properties of Data

Sample vs Population

Types of Sampling Techniques

Exploratory Data Analysis

Analysing Data with Applied EDA

Measures of Dispersion

Skewness & Normality

Correlation & Scatterplot

Barplot & Lineplot

MindMap for Interview Revision

0/11

Data Science Interview Revision Mindmap

Statistics Mindmap

Python Learning Mindmap

SQL Interview Mindmap

Machine Learning Accuracy Improving Techniques Mindmap

Machine Learning Math Mindmap

Real-Time Project Procedure Mindmap

ML Framework Mindmap

Power BI Interview Mindmap

NLP Interview Mindmap

Deep Learning Interview Mindmap

Interview Preparation

0/5

How to build Linkedin Profile

01:10:27

Resume Preparation for Data Science

52:37

Do’s and Don’t while Marketing our Resume

12:54

Do’s and Don’ts in LinkedIn/Job Portals

09:47

3 Elements to Crack Any Interview

11:57

Course Files

0/1

Course Files

Handwritten Notes

0/2

Python Handwritten Notes

SQL Handwritten Notes

Introduction to Deep Learning & Tensorflow

0/8

Building Blocks of Artificial Neural Networks

What are Optimisers and Types of Optimisers

Math Behind Feed Forward Neural Network

Math Behind BackPropagation

Why Activation Functions & Importance

Introduction to Tensorflow

FFNN-Regression Script using Tensorflow

FFNN – Classification Script Using Tensorflow

Feed Forward Neural Network Coding

0/5

Lecture-5

Lecture-6

Back Propagation

Feed Forward Script Explanation

Introduction to Tensorflow & Feed Forward Neural Network

Convolution Neural Network – Math & coding

0/12

Introduction to CNN & Why CNN Algorithm

What is Object Detection & Object Classification

Intuitive Understanding Behind CNN

Math Behind CNN

CNN Coding using Tensorflow

CNN Coding using Keras

Types of CNN Networks

What is Transfer Learning

Tensorflow Hub & Various Pre-Trained Models

Using Pre-Trained Models for Object Classification

Deploying Image Classification Model in AWS

Project Demonstration: Image Classification Project

RNN & LSTM Coding & Math

0/8

What is RNN & Why RNN?

Limitations of RNN & Why LSTM

Intuitive Understanding Behind RNN & LSTM

Math Behind RNN

Math Behind LSTMS

Math Behind GRUs

Coding RNN, LSTM, GRU using Tensorflow

Project Demonstration: Forecasting Project using LSTMS

Mastering NLP

0/13

Introduction to NLP & NLP Applications

NLP Challenges

NLP Pre-Processing Techniques

NLP Feature Extraction

Project Demonstration: NLP Classification Project

What is N-Gram & Why N-Gram Technique

Coding N-Gram Technique

What is Word2Vec?

Word2Vec Algorithms like CBOW & Skip-Gram

Case Study: Coding Word2Vec Model

What is latent dirichlet allocation? Why LDA

What is Topic Modelling?

Case Study: Coding Topic Modelling

Mastering NLU

0/17

What is NLU & Why NLU

What is POS Tagging

Case Study: Coding POS Tagging

What is Dependency Parsing?

What is Constituency Parsing?

Case Study: Coding Dependency & Constituency Parsing

What is NER? & Usage of NER

Case Study: Coding NER

What is Coreference Resolution?

History of NLU Models

Project Demonstration: NLU Classification Model using LSTM

What is Encoder & Decoder Model

Math Behind Encoder-Decoder Models

Case Study: Coding Encoder-Decoder Model

What is Attention Based Models

Math behind Attention Based Models

Case Study: Coding Attention Based Models

Transformer Models BERT, T5, ELMO

0/14

Introduction to Transformers

Scaled Dot Product Attention

Multi-Headed Attention

Introduction to Transfer Learning

Introduction to PyTorch

Fine-Tuning Transformers using PyTorch

Introduction to BERT

Wordpiece Tokenisation

Embeddings of BERT

Masked Language Modelling Task

Project Demonstration: Sentence Prediction Task

Types of BERT

BERT for Sequence Classification

BERT for Question & Answering

Natural Language Generation Using GPT

0/7

Introduction to GPT

Masked Multi-Headed Attention

Pre-Training GPT

Project Demonstration: GPT for Style Completion

Few Shot Learning

Project Demonstration: GPT code Dictation

GPT Multiple Tasks at once with Prompt Engineering

Mastering T5

0/3

Encoder Decoder T5 Architecture

Cross Attention

T5 for Abstractive Summarisation

Vision Transformers

0/2

Introduction to Vision Transformers

Project Demonstration: Fine-Tuning Image Captioning

MLOps for Transformer Models

0/4

Introduction to MLOps

Model Sharing on HuggingFace

BERT Model Deployment using FastAPI

Modern Language Models like GPT-3 & ChatGPT

Autoencoders

0/4

What are Autoencoders?

Types of Autoencoders

Math Behind Autoencoders

Project Demonstration: Coding Different Types of Autoencoders

Generative Models

0/10

What are Generative Models

Math Behind Generative Models

Project Demonstration: GANs

Types of GANs

What are Restricted Boltzmann Machines?

Math Behind Restricted Boltzmann Machines

Project Demonstration: Coding RBMs with Case Study

What is Neural Style Transfer?

Math Behind Neural Style Transfer

Project Demonstration: Coding of NST

Deep Reinforcement Learning

0/17

What is Reinforcement Learning

RL versus ML Framework

Q-Learning & Q-Table Theory

Implementation of Q-Learning

Reinforcement Learning Based Q-Learning

Introduction to Rules of Frozen Lake

Implementation of Frozen Lake

Hyper Parameter Tuning

Introduction to SARSA(State-Action-Reward-State-Action)

Implementation of SARSA

Introduction to Deep Reinforcement Learning DQN

Implementation of DQN

Project Demonstration: DQN Project

AWS Sagemaker Deployment & Project

0/1

Lecture-11

Real-Time Projects

0/1

Real-Time Projects on Image Recognition, Transformers, GANs, Reinforcement Learning

Full Stack AI – #BuildExperience Program

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