#Data Science

Azure Data Engineer is a stable job role responsible for design of Data Warehouses (DWH). This ever promising job stream involves Extraction (E) of data from various sources, perform data mashup and Transformations (T) and Loading the data (L) into Warehouse and Lakehouse platforms.

✅ Database & SQL Concepts
✅ Exploratory Data Analysis (EDA)
✅ Statistics, Probability & Hypothesis Testing
✅ AI & ML Programs
✅ Deep Learning, TensorFlow & PyTorch
✅ Natural Language Processing (NLP)
✅ Model Deployment & MLOps Practices
✅ Big Data with PySpark
✅ Cloud Integrations, Computer Vision
✅ End-to-End Real-Time Data Science Projects

Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Data Engineer Job Roles

Introduction to Data Engineer
Data Science Engineer Job Roles
Data and Databases Concepts

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

Creating Databases (GUI)
Creating Tables, Columns (GUI)
SQL Basics (DDL, DML, etc..)
Creating Databases, Tables
Data Inserts (GUI, SQL)
Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop, Add, modify, etc..
DML: Insert, Update, Delete, select into, etc..
DQL: Fetch, Insert… Select, etc..
SQL Operations: LIKE, BETWEEN, IN, etc..
Special Operators

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

Data Imports with Excel
SQL Native Client
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 7: Schemas & Batches

Schemas: Creation, Usage
Schemas & Table Grouping
Real-world Banking Database
2 Part, 3 Part & 4 Part Naming
Batch Concept & “Go” Command

Ch 8: Constraints, Keys & RDBMS – Level 1

Null, Not Null Constraints
Unique Key Constraint
Primary Key Constraint
Foreign Key & References
Default Constraint & Usage
DB Diagrams & ER Models

Ch 9: Normal Forms & RDBMS – Level 2

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Adding Keys to Tables
Cascading Keys
Self Referencing Keys
Database Diagrams

Ch 10: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 11: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 12: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations

Ch 13: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 14: Triggers & Automations

Need for Triggers in Real-world
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Disabling DMLs & Triggers

Ch 15: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 16: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

Indexes & Tuning
Clustered Index, Primary Key
Non Clustered Index & Unique
Creating Indexes Manually
Composite Keys, Query Optimizer
Composite Indexes & Usage

Ch 18: Group By Queries

Group By, Distinct Keywords
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage
Group By with UNION
Group By with UNION ALL

Ch 19: Joins with Group By

Joins with Group By
3 Table, 4 Table Joins
Join Queries with Aliases
Join Queries & WHERE, Group By
Joins with Sub Queries
Query Execution Order

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries, Joins, Where
Correlated Queries

Ch 21: Cursors & Fetch

Cursors: Realtime Usage
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

IIF Function and Usage
CASE Statement Usage
Window Functions (Rank)
Row_Number( )
Rank( ), DenseRank( )
Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

Merge Statement
Upsert Operations with Merge
Matched and Not Matched
IIF & CASE Statements
Merge Statement inside SPs
Merge with OLTP & DWH

Module 2: Power BI

Ch 1: Power BI Intro, Installation

Power BI & Data Analysis
5 Design Tools, 3 Techniques
2 Hosting Solutions
Power BI with Co-Pilot & AI
Power BI Installation

Ch 2: Report Design Concepts

Basic Report Design (PBIX)
Get Data, Canvas (Design)
Data View, Data Models
Data Points, Spotlight
Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

Visual Interactions & Edits
Limitations with Visual Edits
Creating Power BI Templates
CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

Creating Groups : Lists
Creating Groups: Bins
List Items & Group Edits
Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

Slicer Visual in Power BI
Slicer: Format Options
Single Select, Multi Select
Slicer: Select All On / Off
Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

Hierarchies: Creation, Use
Hierarchies: Advantages
Drill Up, Drill Down
Conditional Drill Down
Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

Power BI Filters
Basic, Top & Advanced
Visual Filters, Page Filters
Report Level Filters, Clear Filter
Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

Power BI Bookmarks
Images: Actions, Bookmarks
Buttons: Actions, Bookmarks
Page to Page NavigationsScore Cards, Master Pages

Ch 9: SQL DB Access & Big Data

SQL DB Access, Queries
Storage Modes: Direct Query
Formatting & Date Time
Storage Modes in Power BI
Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

Charts, Bars, Lines, Area
TreeMaps & HeatMaps
Funnel, Card, Multrow Card
PieCharts & Waterfall
Scatter Chart, Play Axis
Infographics, Classifications

Ch 11: Power Query Introduction

Power Query (Mashup)
ETL Transformations in PBI
Power Query Expressions
Table Combine Options
Merge, Union All Options
Close, Apply & Visualize

Ch 12: Power Query : Table Tfns

Table Duplicate, Header Promotion
Group By Transformation
Aggregate, Pivot Operation
Reverse Rows, Count Rows
Advanced Power Query Mode

Ch 13: Power Query: Column Tfn

Any Column Transformations
Data Type Detection, Change
Rename, Replace, Move
Fill Up, Fil Down
Step Edits & Rollbacks

Ch 14: Power Query: Text, Date

String / Text Transformations
Split, Merge, Extract, Format
Numeric and Date Time
Add Column & Expressions
Expressions and New Columns
Column From Examples

Ch 15: Power Query: Parameters

Parameters in Power Query
Static Parameters, Defaults
Dynamic Dropdowns, Lists
Linking with Table Queries
Step Edits, Type Conversions

Ch 16: Power BI Cloud: Publish

Power BI Cloud Concepts
Workspace Creation, Usage
Report Publish Cloud
Report Edits in Cloud
Semantic Models & Usage

Ch 17: Power BI Cloud Dashboards

Power BI Dashboards
Dashboard Creation, Usage
Pin Visuals, Pin LIVE Pages
Add Image, Video Tiles
Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

Report Shares, Alerts
Subscriptions, Exploration
Downloads & Edits
Report Cloning in Cloud
QR Codes, Web Publish
Lineage & Metrics

Ch 19: Power BI Cloud Gateways

Data Gateways, Data Refresh
Install, Configure Gateways
Data Sources Configurations
Data Refresh & Scheduling
Gateway Optimizations

Ch 20: Power BI Cloud Apps

Power BI Apps: Creation
App Sections & Content
Audience Options
App Security & Sharing
App Updates, Favourites
App URL, End User Access

Ch 21: Power BI Report Server

SQL Server 2025 (Mandatory Installations)
Power BI Report Server
Report Server Vs Cloud
Installation, Configuration
RS Config Tool Options
Report Database, TempDB
Web Service & Server URL

Ch 22: Paginated Reports

Report Builder Tool
Paginated Report (RDL)
Report Expressions (RDL)
Tablix, Chart Wizards
Fields & Drill-Down
RDL Report Publish

Ch 23: DAX Concepts (Basics)

DAX Concepts: Intro & Realtime Need
DAX Columns: Creation, Use
DAX Measures: Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions
DAX Cheat Sheet

Ch 24: DAX Quick Measures

Quick Measures in Power BI
Average & Filters
Running Totals
Star Rating Calculations
DAX Measures in Data View
DAX in Visuals
DAX in Cloud Reports

Ch 25: Data Modelling, DAX

Dimensions Tables
Fact Tables & DAX Measures
Data Models & Relations
DAX Expressions
Star & Snowflake Schemas
DAX Joins & Expressions

Ch 26: DAX Joins, Variables

CALCULATEX & Variables
COUNT, COUNTA, etc..
SUM, SUMX, etc..
SELECTED MEMEBER
Filter Context, RETRUN
Dynamic Report with DAX

Ch 27: DAX Time Intelligence

Need for Time Intelligence
Date Table Generation
Time Intelligence with DAX
PARALLELPERIOD, DATE
CALENDAR, Total Functions
YTD, QTD, MTD with DAX

Ch 28: DAX – Row Level Security

RLS: Row Level Security
Data Modelling & Roles
Verify Roles (Testing)
Add Cloud Users to Roles
Dynamic Row Level Security
Testing RLS in Power BI

Ch 29: Analytical Reports

Analytical Report Concepts
Excel Data Analytics
Excel with Power BI Cloud
SQL, AVRO, JSON Sources
Analyse in Excel (Cloud)
Excel Reports to Cloud

Ch 30: Power BI AI, CoPilot, Projects

👉🏻 AI Components in Power BI
👉🏻 CoPilot Practical Uses
👉🏻 CoPilot with Desktop
👉🏻 CoPilot with Cloud
👉🏻 Need for AI Analytics (Fabric)
👉🏻 PL 300 Exam (Microsoft Certified Data Analyst) Guidance
👉🏻 PL 300 Exam Mocks

Module 3: Python For Data Analysis

Ch 1: Python Introduction

Python Introduction
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 2: Python Operations

Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Python: Internal Architecture
Compiler Versus Interpreter

Ch 3: Data Types & Variables

Integer / Int Data Types
Float, String Data Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()

Ch 4: Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 5: Python Loops, Iterations

Python Loop & Realtime Use
Python While Loop Statement
Break and Continue Statement
Iterations & Conditions
Exit Conditions & For Loops
iter() and Looping Options

Ch 6: Python Functions

Python Functions & Usage
Function Parameters
Default & List Parameters
Python Lambda Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 7: Python Modules

Import Python Modules
Built In Modules & dir
datetime module in Python
Date Objections Creation
strftime Method & Usage
imports &now()

Ch 8: Python User Inputs & TRY

Try Except, Exception Handling
Raise an exception method
TypeError, Scripting in Python
Python User Inputs
Python Index Numbers
input() & raw_input()

Ch 9: Python File Handling

File Handling, Activities
Loop, Write, Close Files
Appending, Overwriting
import os,exists
open, f.write
read, f.close

Ch 10: Pandas DataFrames 1

Installation of Pandas
Python Modules & Pandas
Pandas Codebase & Usage
import DataFrame
Pandas Series, arrays

Ch 11: Pandas DataFrames 2

Indexes & Named Options
Locate Row and Load Rows
Row Index & Index Lists
Load Files Into a DataFrame
to_string() Function
tail() & null() Function

Ch 12: Pandas Transformations

Pandas – Cleaning Data
Replace, Transform Columns
Data Discovery & Column Fill
Identify & Remove Duplicates
dropna(), fillna() Functions
Data Plotting & matlib Lib

Ch 13: Realtime Project (Banking / Finance) For Data Analysis [End to End]

Ch 14: Weekly Mock Interview Review meet ++ 1:1 Resume Meet

Module 4: Python Programming

Ch 1: Python Dictionary

Dictionary Creation, Use
Hashing, Copy, Update
Deletion, Sorting
Len(), Inbuilt Functions
Variable Types – python List
Cmp() List Method
Python Dictionary Str(dict)
Programming Concepts
Loops and Sets
Realtime Usage

Ch 2: Python Packages

Package in Python
Creating a package
Package Imports, Modules
Sub Packages Creation
Sub Package Imports
Popular Packages in Python
NumPy & SciPy
Libraries in Python
Python Seaborn
Python framework

Ch 3: Exception Handling

Shell Script Commands
OS operations in Python
File System Shell Methods
os – math – cmd -csv – random
Numpy (numerical python)
Pandas – sys – Matplotlib;
Common RunTime Errors
Python Custom Exception;
Exception Handling

Ch 4: Python Class & Objects

Class variables, Instances
Built in Class Attributes
Objects – Constructors
Modifiers – Self Variable
Python Garbage Collections
Hierarchical Inheritance
Multilevel, Multiple, Hybrid
Overloading & OverRiding
Polymorphism – Abstraction

Ch 5: Regular Expressions

Regular Expression
Regular Expression Patterns
Literals – Repetition Cases
Groups andGrouping
w+ and ^ , \s Expressions
split function
Regular expression methods
match() in Regular Expr
search(), re.findall for Text

Ch 6: Multi-Threading

Python Multi-Threading
Thread Synchronization
Multiprocessing
Python Gil & Programming
Thread Control Block (TCB)
Stack Pointers & App Usage
Program Counters in Realtime
Thread State Concept
Python Exception Handling

Ch 7: Python TKinter

Tkinter GUI Program
Components & Events
Adding Controls inTkinter
Entry, Text Widgets
Radio & Check Buttons
Tkinter Forms in Realtime
List Boxes, Menu, ComboBox
Mainloop () & Functions

Ch 8: Python Web & IoT Intro

Python Web Frameworks
Django : Advantages
Web Framework
MVC and MVT – Django
Web Pages using python
HTML5, CSS3 usage
PYTHON Bottle & Pyramid
Falcon ; smart_open in python

Module 5: Python with AI – ML

Ch 1: Machine Learning Basics

Machine Learning Funda
Python ML in Realtime
Pandas Extension in ML
Machine Learning Ops
Business to Data Conversions
ML Algorithms in Realtime

Ch 2: Python ML Concepts

Machine Learning (ML) Intro
Supervised, Unsupervised
Scikit-Learn Library
Python Libraries for ML
sklearn : Advantages & Uses
sklearn : Functions, Use

Ch 3: Python Data Handling

Data structures
Lists, Tuples, Sets
Dictionaries,
Pandas Data Operations
Data Visualizations
Matplotlib & Seaborn

Ch 4: AI With Python Intro

Artificial Intelligence
Applications of AI
AI Applicative Uses
AI Usage with Python
AI – Python Environment
Python Libraries
AI with Python in Realtime

Ch 5: Supervised Learning

Linear & Logistic Regression
Decision Trees
Random Forests
Support Vector Machines
Neural Networks Basics
Linear Regression Steps
Linear Regression in AI-ML

Ch 6: Unsupervised Learning – 1

Clustering & K-means
DBSCAN & Realtime Usage
Dimensionality Reduction
K clustering hierarchical
DBScan : Realtime Uses
KMeans clustering Vs DBSCAN?
PCA Vs t-SNE

Ch 7: Unsupervised Learning 2

Unsupervised Learning
Concepts and Scope
Realtime Usage
Dimensionality Reduction
Component Analysis (PCA)
PCA: Concept & Usage

Ch 8: Generalized Models

GLM Concept in Python
GLM in Regression
Considerations for GLM
Problem Solving Skills
Python Libraries
Python Extensions: GLM

Ch 9: Python Tree Models

Decision Tree Models
Decision Tree Working
Model Works, Algorithms
Random Forest Concept
Random Forest Tree
Random Forest Vs Knn

Ch 10: Big Data and ML

Spark and Big Data
Big Data with Python
Spark with Python
Spark with Big Data
Spark Algorithms
AI ML Libraries

Ch 11: Natural Lang” Processing

NLP : Purpose, Usage
NLP Applicative Uses
NLP Vs Machine Learning
NLP in Machine Learning
Using NLP in AI – ML
NLP code in Python?

Ch 12: AI in Real-World

AI in Chatbots
AI in Virtual Assistants
AI Ethical Considerations
AI Deployments (Flask)
AI with FastAPI
AI with Streamlit

Module 6: Advanced Statistics

Ch 1: Introduction to Statistics

Importance of Statistics in DS & AI
What is Statistics?
Population and Sample
Parameter and Statistic
Data Types
Importance of Statistics in DS & AI
What is Statistics?
Population and Sample
Parameter and Statistic
Data Types

Ch 2: Basic Operations

What is Data, Its type and Data
What is Univariate and BI Variate Analysis?
Measures of Central Tendencies
Mean, Median, & Mode
Measures of Dispersion
Variance, Standard Deviations
Range, & Interquartile Range
Covariance and Correlation
Box Plots and Outliers detection
Skewness and Kurtosis

Ch 3: Probability

Probability And Limitations
Axioms Of Probability
Conditional Probability
Random Variable
Discrete Probability Distributions – Probability Mass Functions
Bernoulli, Binomial Distribution, Poisson Distribution
Continuous Probability Distributions – Probability Density Functions
Normal Distribution, Standard Normal Distribution

Ch: 4 Data Sampling

Data Collection Techniques
Sampling Techniques:
Convenience Sampling, Simple Random Sampling
Stratified Sampling, Systematic Sampling and Cluster Sampling

Ch 5: Inferential Statistics

Sampling variability and Central Limit Theorem
Confidence Intervals
Hypothesis Testing, A/B testing
parametric vs non-parametric tests
test for normality
Z -test, t-test
Chi – Square Test
F -Test and ANOVA

Ch 6: EDA

What is EDA?
Uni – Variate Analysis
Bi – Variate Analysis
Usage of Seaborn for Pair plots, Heat Maps, Count Plot
Introduction to Web Scrapping

Ch 7: Advanced Regex for Unstructured Data

Structured Data and Unstructured Data
Literals and Meta Characters
How to Regular Expressions using Pandas?
Inbuilt Methods
Pattern Matching
flags

Module 7: Machine Learning (Advanced Level)

Ch 1: Introduction

What is Machine Learning?
Types of learning
Approaches of machine learning algorithms
Decision boundaries
data pre-processing
Under fit, optimal fit, over fit
sklearn pipeline + model building

Ch 2: Validation Methods

Cross-Validation
The Validation Set Approach Leave-One-Out Cross-Validation
k -Fold Cross-Validation
Bias-Variance Trade-Off for k-Fold Cross-Validation

Ch 3: Naïve Bayes

Principle of Naive Bayes Classifier
Bayes Theorem
Terminology in Naive Bayes
Posterior probability
Prior probability of class
Likelihood
Types of Naive Bayes Classifier
Multinomial Naive Bayes
Bernoulli Naive Bayes and Gaussian Naive Bayes
Categorical naive bayes

Ch 4: Linear Algebra

Introduction to Matrices
Vector spaces
Eigenvalues and Eigenvectors

Ch 5: K Nearest Neighbours

K-Nearest Neighbour Algorithm
Eager Vs Lazy learners
How does the KNN algorithm work?
How do you decide the number of neighbours in KNN?
Weighted knn, ball tree, kd tree, lsh forest, cosine hashing
Curse of Dimensionality
Pros and Cons of KNN
How to improve KNN performance
Hyper parameters of knn

Ch 6: Decision Trees

Basic Terminology in Decision Tree
Root Node and Terminal Node
Classification Tree
Regression tree
Trees Versus Linear Models
Advantages and Disadvantages of Trees
Gini Index
Overfitting and Pruning
Stopping Criteria
Accuracy Estimation using Decision Trees
Hyper parameter tuning using random search, grid search, kfold cv

Ch 7: Linear Regression

Simple Linear Regression:
Estimating the Coefficients
Assessing the Coefficient Estimates

Ch 8: Multiple Linear Regression

Estimating the Regression Coefficients
OLS Assumptions
Multicollinearity
Feature Selection
Gradient Discent

Ch 9: Evaluation metrics for Regression

Homoscedasticity and Heteroscedasticity of error terms
Residual Analysis
Q-Q Plot
Identifying the line of best fit
R Squared and Adjusted R Squared
M SE and RMSE

Ch 10: Polynomial Regression

Why Polynomial Regression
Creating polynomial linear regression
Evaluating the metrics

Ch 11: Regularization Techniques

Lasso Regularization
Ridge Regularization
ElasticNet Regularization

Ch 12: Logistic Regression

An Overview of Classification
Difference Between Regression and classification
Why Not Linear Regression?
Logistic Regression:
The Logistic Model
Estimating the Regression Coefficients and Making Predictions
Logit and Sigmoid functions
Setting the threshold and understanding decision boundary
Logistic Regression for >2 Response Classes
Evaluation metrics for classification models

Ch 13: Support Vector machine

The Maximal Margin Classifier
HyperPlane
Support Vector Classifiers and Support Vector Machines
Hard and Soft Margin Classification
Classification with Non-linear Decision Boundaries
Kernel Trick
Polynomial and Radial
Tuning Hyper parameters for SVM
Gamma, Cost and Epsilon
SVMs with More than Two Classes

Ch 14: Ensemble Methods in Tree Based Models

What is Ensemble Learning?
What is Bootstrap Aggregation Classifiers and how does it work?
Series vs parallel ensemblers

Ch 15: Random Forest Algorithm

What is it and how does it work?
Variable selection using Random Forest

Ch 16: Boosting

Introduction to adaboost, Gradient Boosting, XG Boosting
Working with the alogorithms
Pros and Cons

Ch 17: ML for Data Analysis

Missing Value imputation using Machine Learning Algorithms
Outlier and Anomalies detection using Machine Learning Algorithms

Ch 18: Un-Supervised Learning

Why Unsupervised Learning
How it Different from Supervised Learning
The Challenges of Unsupervised Learning

Ch 19: Dimensionality Reduction

Introduction to Dimensionality Reduction and it’s necessity
What Are Principal Components?
Demonstration of 2D PCA and 3D PCA
EigenValues, EigenVectors and Orthogonality
Transforming Eigen values into a new data set
Proportion of variance explained in PCA
t-Distributed stochastic neighbor embedding (t-sne)

Ch 20: K-Mean Clustering

Centroids and Medoids
Deciding optimal value of ‘k’ using Elbow Method
Linkage Methods
Clustering metrics – Silhouette score

Ch 21: Hierarchical Clustering

Divisive and Agglomerative Clustering
Dendrograms and their interpretation
Applications of Clustering
Practical Issues in Clustering

Ch 22: Other Learning ways in ML

Reinforcement Learning
Hybrid Learning models
Other models

Module 8: Deep Learning, Computer Vison

Ch 1: Introduction

Introduction to AI , ML AND DL
Difference between ML and DL
When to use ML and DL
History Of Deep Learning
Introduction to Biological Neuron

Ch 2: Neural Networks

Introducing Google Colab
Tensorflow basic syntax
Tensorflow Graphs
Tensorboard

Ch 3: Forward and Backward Propogation

MLP Architecture
Defining the Notation for MLP
Working of MLP (Forward Propagation
How To Train Single Neuron Model
Backpropagation -1 (chain rule)
Backpropagation -2 (chain rule+ memorization)
Hyperparameter In MLP
Bias and Variance Trade-off In MLP
Why Deep Neural Network Failed
Activation Function -1 (Sigmoid)
Activation Function -2 (Tanh)
Vanishing Gradient Problem
Exploding Gradient Problem
Activation Function -3 (ReLU and ReLU Variants Linear and Non Linear Variants)
Weight Initialization Techniques (pros and cons)
Batch Normalization
Early Stopping

Ch 4: Optimizers

Convex Function And Non Convex Functions ,Saddle Point
SGD with Momentum
NAG
Rmsprop
Ada Delta
Ada Grad
ADAM
NADAM

Ch 5: Keras

Intro To Tensorflow and Keras
Working

Ch 6: Images and image processing with OpenCV

Intro To Images
How Images are formed and stored in machines
Color Spaces
Intro To OpenCv
read, write, save image
Converting to Different Color Spaces
Building Histograms for Images
Read videos
Capturing images with web camera
Manipulating videos with opencv
Drawing on images and videos
Bitwise Operators On Images and Videos
Affine and Non-Affine Transformation

Ch 7: CNN

Intro To CNN
Why CNN over MLP
How does Convolution works on images
Padding, Stride, Pooling
LeNet5
Alex Net
Vgg 16 and Vgg 19
Inception Net
ResNet
Xception
Mobile Net
Efficient Net
Pre trained Model Introduction

Ch 8: Transfer Learning

Intro To Transfer Learning
Transfer learning Concepts (When and Why)
Transfer Learning Coding
Hyper Parameter Tuning [Random Search, Hyperband, Bayesian optimization]
Ch 4: Object Detection
Intro To object Detection
R-CNN
Fast R-CNN
Faster R-CNN # Show why Faster R CNN is faster than R CNN ( no Need of Maths)

Ch 9: YOLO

Intro to Yolo
Yolo Algorithm (How it works) – More Detail on YOLO
Implementation of Yolo V7 / V8 using Ultralytics

Module 9: Natural Language Processing

Ch 1: Introduction to text processing

Intro to NLP
Text Preprocessing Steps
Tokenization
Special Character
Stop words
Stemming & Lemmatization

Ch2: Vectorization Techniques

BOW
TF-IDF
Coding for BOW and TF-IDF using nltk
Word2Vec
How Word2Vec algorithm works (Skip-Gram & CBOW)
Glove
FastText

Ch 3: RNN

Intro to RNN
Why RNN ?
How RNN Works
Trainning RNN
Types of RNN

Ch 4: LSTM

Intro to LSTM
Why LSTM
LSTM algorthim
Grus
Bi-Directional RNN
Understanding of working of Image captioning

Ch 5: Auto Encoders

Encoder Decoder Architecture
Introduction to autoencoders
Types of autoencoders

Ch 6: Transformer and attention

Intro to Transformers and Attention Models
How does Transformers works
How does Attention works
Coding For Transformers and Attention Models

Ch 7: BERT

Intro to BERT
How does BERT works
Coding For Transformers and Attention Models

Module 10: AI & Gen AI, Prompt Engineering

Ch 1: Introduction

Introduction to Generative AI
Overview of generative AI
Applications and case studies across industries

Ch 2: Introduction to LLM

History of NLP
Into to large language Models
What is Large Language Model
Types of Large Language Model

Ch 3: Prompt Engineering

Introduction
Working with models

Ch 4: Popular Gen-AI application

Intro To Open AI and Gemini
Utilizing OpenAI APIs
Setting up and authenticating API usage
How to obtain an API key for
Overview of the Gemini API and accessing its
Detailed exploration of different Gemini
Selecting and initializing the right model for specific tasks

Ch 5: LLaMA

Introduction of LLaMA
Comparison with other large language models like GPT-3 and GPT-4
Key features and capabilities of LLaMA
Understanding the Model Architecture of
Discussion on model sizes and
Environment setup: Installing necessary libraries and tools
Accessing LLaMA models: Overview of the download process and setup on local machines or cloud platforms (Meta LLaMa)
Intro to the architecture of LLaMA models
Understanding the differences between LLaMA model variants (8B, 13B, 30B, and 70B parameters)
Implementing text generation using LLaMA