What is Data Analytics, DS, AI/ML

Course Content

ML Real-Time Projects Pipelines

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End-to-End ML Project with Amazon S3, Snow Flake, Spark & Snow Park

End-to-End ML Project Using Spark & Microsoft Fabric

AGILE CRISP: End-to-End ML Project Pipeline

Agile DataOps for Real-Time Credit Risk Scoring and Analysis

MLOps Pipeline: End-to-End Data Science Lifecycle using MLOps

Streaming Augmented Analytics: End-to-End ML Project Pipeline

Internship Projects To Build Portfolio(Place them in Resume)

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ML End-to-End Real-Time Project-1

ML End-to-End Real-Time Project-2

ML End-to-End Real-Time Project-3

ML End-to-End Real-Time Project-4

Real-Time NLP Project-1

Real-Time NLU Project-2

Real-Time Prompt Engineering Project with Chatbot Development

Real-Time RAG Architecture LLM using LangChain, BedRock Project

Mastering Python[Zero to Hero]

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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

Advanced Data Structures and Algorithms

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Introduction to 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

Applied Statistics

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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

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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 Distributions, Hypothesis Testing

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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 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

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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

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

Machine Learning Algorithms with Deep Math

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

Mastering NLP, NLU, NLG

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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

ML Deployment & MLOps

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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

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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

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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

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Setting up AWS Sagemaker & Deploying Simple ML Model

15:13

AIOps with AWS Sagemaker Studio

01:02:44

Deep Learning, Tensorflow & Keras

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Introduction to Neural Networks

14:02

What are Optimisation Techniques

14:32

Math Behind Feed Forward Neural Networks

10:13

Back Propagation Techniques

12:12

Why Activation Functions

11:11

Regression using Tensorflow

18:36

Feed Forward Classification using Keras

11:41

Computer Vision Mastering using Tensorflow

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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

02:44:06

Transfer Learning with OpenCV & YOLO

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Introduction to Transfer Learning

Headless & With Head Transfer Learning

Object Detection using YOLO

Object Detection using Open CV

Transfer Learning with Tensorflow Hub

Introduction to Generative AI

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What is Generative AI?

Generative Models & Discriminative Models

Types of Generative Models

Applications of Generative AI

How to Automate Work with Generative AI?

What is Responsible AI?

What is Representation Learning?

Generative AI End-to-End Project Lifecycle

Open AI – Mastering

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Introduction to Open AI

Creating Open AI Keys

Implementing Vision Based Generative Models using Open AI

Implementing Text Based Generative Models using Open AI

Speech to Text Implementation with Open AI

Fine-Tuning LLMs with Custom Data

Mastering LangChains

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Introduction to LangChains

LangChains Architecture

Components of Langchains

Fine-Tuning Prompts with LangChains

ChatBot with LangChains

Q & A with LangChain

Text Summarisation with LangChain

Different Prompt Template with Langchain

Document and File Analysis with LangChains

Hugging Face Models with LangChains

Falcon Fine-Tuning

Mistral Fine-Tuning on Custom Data

LLaMA2 with LangChains

PalM2 with LangChains

LangChain with Streamlit & Flask Deployment

Prompt Engineering with LangChains and Open AI

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What is Prompt Engineering?

Types of Prompt Engineering

Zero, Single & Few Shot Prompt Engineering

Implementing Prompt Templates

Reducing Hallucination Rate with Prompting

Providing Context with Prompts

Emotional Based Prompts

End-to-End Project Prompt Engineering Implementation using LangChains & Open AI

Generative AI End-to-End Projects

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Document Summariser Project using LangChains, Open AI, Streamline & AWS Deployment

Medical Q & A Generative Model with LangChain, Open AI, Streamline & AWS Deployment

Product Recommendation Generative Model using LLaMA2, Streamlit, LangChain & AWS

ChatBot with LangChain, Streamlit, Open AI & AWS Deployment

PySpark: BigData Processing

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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

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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

Hadoop

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Introduction to Hadoop

Understanding HDFS

Using HDFS Command Line

Using HDFS Web Interface

Hadoop MapReduce

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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

R Programming

0/9

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

Mindmaps for Interview Revision

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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

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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

ML Career Transition Program with MLOps

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