What is Data Analytics, DS, AI/ML

Foundational Math(Induction Classes)

0/8

Vectors, Matrices, Tensors

Matrix operations: Addition, Multiplication, Transposition

Eigenvalues, Eigenvectors, Singular Value Decomposition (SVD)

Dot product, Matrix Inversion

21:23

Functions, Limits, Derivatives

Partial Derivatives & Gradient

02:27

Chain Rule (Backpropagation)

Integration basics

Mastering Python for AI

0/37

Introduction to Python

Why Python, Value, Variable, Function, Library

IDE in Python, Different Data Types

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, Generator Expressions, 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

EDA 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

Mastering OpenCV

Working with Beautiful Soup

Hands-On Tensorflow

Hands-On PyTorch

Hands-On LangChain

Hands-On Scikit-Learn Library

Hands-On Scipy

Advanced Data Structures and Algorithms

0/23

Introduction to Advanced DSA

Non-Primitive Data Structures

Non-Linear Data Structures

What is an Algorithm

Theory & Code Implementation of Linked List

Stacks & Queues Assignment

Coding Stack Data Structure

Coding Queue Data Structure

Tree Data Structures

Types of Tree Data Structure

Tree Traversal

BFS Traversal

Bubble Sort Theory

Bubble Sort Code Implementation

Selection Sort Theory

Selection Sort Code Implementation

Insertion Sort Theory

Insertion Sort Code Implementation

Merge Sort Theory

Merge Sort Code Implementation

Quick, Merge Sort Performance

Quick Sort Theory

Linear Search & Bisection Search

Hands-On SQL, Projects with Data Warehouse Concepts

0/19

What is SQL, RDBMS, and Table structure

Understanding Sprint, Scrum and Agile Project Breakdown in SQL

OLTP vs OLAP

Data Warehousing Concepts

15:42

Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)

29:03

ER Diagrams

18:39

Data Models like Star Schema and Snowflake Schema

DDL vs DML vs DCL vs TCL commands

Basic CRUD Operations

41:21

Different DDL Commands

41:22

Different DML Commands

Upsert Operations

Different DQL Commands

Database Constraints

Aggregate Functions, Date Functions and String Functions

SQL Joins: Inner, Self, Cross, Left, Right and Outer Join

SQL Grouping & Aggregations

SubQueries and Types of SubQueries

Window Functions in SQL

Hands-On Advanced SQL Concepts

0/17

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

Views, Types of Views and Materialized Views

Stored Functions

Stored Procedures

Triggers in MySQL

Create Events in MySQL

Different Functions in MySQL

Set Operations: Union, Intersect, Except

Transaction Control Language

ACID properties

Index tuning, query rewriting

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

Statistics with Real-Time Project Demonstration on EDA

0/4

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

0/8

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

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

Applied Machine Learning(Basics To Job-Ready)

0/39

Types of ML: Supervised, Unsupervised, Reinforcement

Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation

Bias-Variance Trade-off

Train/Validation/Test splits

06:21

Cross-Validation

Linear Regression (with gradient descent math)

Math behind OLS Technique

04:32

Assumptions of Linear Regression

23:08

Evaluation Metrics: RMSE, MAE, R² Score

15:50

Polynomial Regression

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

Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC

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, Gradient Boosting, XGBoost, LightGBM, CatBoost

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

Clustering: K-Means (Euclidean distance), Hierarchical Clustering

Dimensionality Reduction: PCA (Eigen decomposition), t-SNE

Anomaly Detection

Hyperparameter Tuning: GridSearchCV, RandomSearch

Pipelines with Scikit-learn

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

Natural Language Processing

0/14

What is NLP? Applications in industry

Tokenization, Stopwords, Lemmatization vs Stemming Normalization

Regular Expressions (Regex) in NLP

Bag of Words (BoW)

TF-IDF Vectorization (Term Frequency-Inverse Document Frequency)

Word embeddings: Word2Vec (CBOW, Skip-Gram) GloVe

Cosine Similarity

N-Gram models (Unigram, Bigram, Trigram)

Language Modeling: Probability of a word sequence

Smoothing techniques (Laplace, Good-Turing)

Parts-of-Speech Tagging (Rules + Statistical)

Named Entity Recognition (NER)

Chunking and Chinking

Using spaCy’s NER pipeline

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

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

Generative AI, Prompt Engineering using LangChains

0/12

What is Generative AI? Use cases in text, image, code, and conversation

Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA

Introduction to Lang Chains

Using LLMs using Lang Chains

What is Prompt Engineering

Prompt Engineering Principles

Zero-shot, Few-shot, Chain-of-Thought (CoT)

Best Way to Improve LLMs Accuracy

Doing Prompt Engineering using Lang Chains

PromptTemplate best practices with LangChain

Using ChatPromptTemplate, SystemMessagePrompt, HumanMessagePrompt

Prompt evaluation: temperature, top_p, stop, max_tokens

Retrieval-Augmented Generation (RAG) with LangChain

0/11

Why RAG? Token limits, context injection

Vector Embeddings: OpenAI, HuggingFace, Cohere

Vector DBs: FAISS, Pinecone, Chroma, Weaviate, Milvus

LangChain’s RetrievalQA Chain

Chunking strategies and TextSplitter

Hybrid search: Dense + Metadata filtering

SequentialChain vs SimpleSequentialChain

RunnablePassthrough, RunnableLambda (LCEL)

Creating Tool abstractions with @tool decorator

Integrating APIs (e.g., SerpAPI, Weather, Calculator)

Output parsers: StrOutputParser, StructuredOutputParser

Agentic AI – Build Autonomous Agents with LangChain

0/5

What is Agentic AI vs traditional agents

Types of agents in LangChain: ReAct, ConversationalReAct, Plan-and-Execute, OpenAIFunctionsAgent

Agent memory (ConversationBufferMemory, SummaryMemory)

Task Decomposition and Planning

Use Cases: Code generation, Auto-documentation, Research Assistants

Model Context Protocol (MCP) and Multi-Agent Architectures

0/7

What is Model Context Protocol (MCP)

Context-aware decision making

Prompt-as-function vs Function-as-prompt

Designing structured workflows (LangGraph overview)

Communication between agents

Build an MCP-based RAG agent

Implement agent-to-agent communication where one retrieves, another summarizes, and a third decides actions

Fine-Tuning and Pretraining Language Models

0/4

Difference between Pretraining, Fine-Tuning, Instruction Tuning

Fine-Tuning Open Source Models (LLaMA, Mistral, Falcon)

PEFT Techniques: LoRA (Low-Rank Adaptation) Adapters Prefix-Tuning

Tools: HuggingFace Transformers, PEFT, DeepSpeed, bitsandbytes

Full Stack Application + Deployment(LLMOps)

0/5

Combining RAG, Agentic AI, and Fine-tuned models

Deploying LangChain applications on: Streamlit, Solara, FastAPI, Gradio

CI/CD Pipelines for LLMOps

Monitoring and Cost Management (OpenAI + LangSmith + Tracing)

Build an End-to-End Custom Knowledge Assistant: Ingest PDFs/Docs Chunk and Embed to Vector DB Query using Conversational Agent (RAG + Tools + Planner) Deploy using Solara

Advanced Deep Learning

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

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

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

Topic Modelling

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

Autoencoders

0/6

What are Autoencoders?

Types of Autoencoders

Math Behind Autoencoders

Project Demonstration: Coding Different Types of Autoencoders

DeNoising Autoencoders

Variational AutoEncoders

Image Based 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

Diffusion Models, Energy Based Models & NFMs

0/9

Introduction to Diffusion Models

Deep Dive into CLIP Research Paper

Deep Dive into GLIDE Research Paper

Understanding Diffusion Model Theory & Architecture

Project Demonstration on Diffusion Models

Understanding Energy Based Models Theory & Architecture

Project Demonstration on Energy Based Models

Understanding Normalising Flow Models

Project Demonstration on Normalising Flow Models

LLMS: Hugging Face

0/5

Introduction to Hugging Face

End-to-End Pre-Trained Model using Hugging Face

Fine-Tuning BERT Models using Hugging Face

Building LLMs using Hugging Face

Deployment using Hugging Face & StreamLit

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

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

Pyspark: BigData Processing(Smart-Board Access)

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(Smart-Board Access)

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

R Programming(Smart-Board Access)

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

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Course Files

Handwritten Notes

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Python Handwritten Notes

SQL Handwritten Notes

Full Stack Artificial Intelligence Internship Course

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