Online Data Science with Generative AI, Machine Learning, DSA, SQL, Power BI, RAG & Agentic AI Training Course with Internship – 100% Job Placement-Oriented

Accredited & Recognized Excellence

Program Key Benefits

Master Skills: Industry-relevant skills with hands-on training from top corporate professionals.

Eligibility: Open to Any Graduates and individuals with Career Gaps.

Expert Trainers: Led by Mr. Siva • Mr. Mani • Mr. Sharif (15+ Yrs experience), successfully completing 80+ batches.

Program Duration: Comprehensive 6 Months training schedule.

Lifetime Live Classes & Video Access: Lifetime access to live classes and recorded videos for learning, revision, and practice.

Doubt Clarification: Support via Live Classes, WhatsApp, and LMS.

Assessments: Regular Interview Questions and Code Assessments included.

Career Support: Resume Preparation, LinkedIn Profile Creation, and Portfolio Website Development.

Certificates: Free Training Completion Certificate (Soft Copy) provided.

Industry Terms & Tips: Coverage of Agile, Scrum, Monitoring, and corporate career success strategies.

One-on-One Demo: Get Personalized Training & Clarifications before getting started.

Tools & Technologies Covered

Core & Advanced Python Data Structures & Algorithms (DSA) NumPy, Pandas, SciPy & Matplotlib Seaborn & Data Visualization Mathematics for Machine Learning Statistics & Probability SQL & Database Management MySQL MongoDB & NoSQL Data Cleaning & Preprocessing Exploratory Data Analysis (EDA) Feature Engineering & Selection Microsoft Excel Microsoft Power BI & DAX Business Data Visualization Machine Learning Regression & Classification Clustering & Dimensionality Reduction Model Evaluation & Hyperparameter Tuning Time Series Analysis & Forecasting Deep Learning TensorFlow & PyTorch Fundamentals Neural Networks & CNN Computer Vision & OpenCV Natural Language Processing (NLP) Transformers & Large Language Models (LLMs) Generative AI & Prompt Engineering ChatGPT, Microsoft Copilot, Gemini & Grok LLM APIs & Ollama Embeddings & Vector Databases Retrieval-Augmented Generation (RAG) LangChain & Document Chatbots LLM Fine-Tuning Fundamentals LoRA, QLoRA & Quantization Fundamentals LLM & RAG Evaluation Agentic AI & AI Agents LangGraph & Tool Calling n8n Workflow Automation Model Context Protocol (MCP) Fundamentals Flask & FastAPI Streamlit Application Development REST APIs & Model Deployment Git & GitHub AWS Cloud & EC2 Deployment ETL & Data Engineering Fundamentals Data Warehousing Fundamentals Hands-on Assignments & Mini Projects End-to-End AI Capstone Projects 4-Week Guided Project-Based Internship Technical Interview Preparation Resume & GitHub Portfolio Preparation Career Guidance & Placement Support

Complete Course Syllabus

Introduction to Programming and Python
History, Features, Advantages, and Applications of Python
Python vs. Other Programming Languages
Installing Python and Setting Up the Environment
Python Interpreter and Execution Process
Python Interactive Mode and Script Mode
IDEs: IDLE, VS Code, and PyCharm
Writing and Executing the First Python Program

Variables, Keywords, and Identifiers
Naming Conventions and Comments
Literals and Types of Literals
Numeric, String, Boolean, and None Literals
Collection Literals: List, Tuple, Set, and Dictionary
Python Built-in Data Types
Type Checking Using type() and Object Identity Using id()
Type Conversion and Type Casting
Built-in Conversion Functions: int(), float(), str(), bool()
Collection Conversions: list(), tuple(), set(), and dict()
User Input Using input()
Output Using print() and Parameters sep and end
String Formatting: f-strings, format(), and % Formatting
Multiple Assignment and Variable Unpacking
Comments, Indentation, and Basic Python Syntax

Arithmetic Operators
Comparison and Logical Operators
Assignment and Compound Assignment Operators
Bitwise Operators
Membership Operators: in and not in
Identity Operators: is and is not
Difference Between == and is
Operator Precedence and Associativity
Practical Programs Using Python Operators

Introduction to Decision-Making
if Statement
if-else Statement
if-elif-else Statement
Nested if Statements
Conditional Expressions (Ternary Operator)
match-case Statement (Python 3.10+)
Truth Values and Conditions
Real-Time Decision-Making Programs

Introduction to Iteration and Loops
while Loop and for Loop
range() Function and Its Variations
Nested Loops
break, continue, and pass Statements
else with for and while Loops
Loop Control and Infinite Loops
Pattern Programming
Number and Sequence-Based Programs
Real-Time Loop Examples

String Creation and String Literals
Indexing, Negative Indexing, and Slicing
String Immutability and String Operators
String Concatenation and Repetition
String Searching Methods: find(), rfind(), index(), rindex()
Case Conversion Methods: upper(), lower(), title(), capitalize(), swapcase()
String Validation Methods: isalpha(), isdigit(), isalnum(), isspace(), islower(), isupper()
String Cleaning Methods: strip(), lstrip(), rstrip()
String Replacement and Translation Methods
String Splitting and Joining: split(), rsplit(), splitlines(), join()
String Partitioning: partition() and rpartition()
String Prefix and Suffix Checking: startswith() and endswith()
String Formatting: format(), format_map(), and f-strings
String Alignment: center(), ljust(), and rjust()
Raw Strings and Escape Sequences
String Validation, Manipulation, and Processing Programs

Introduction to Python Collections
Lists: Creation, Indexing, Slicing, and Methods
List Operations: append(), extend(), insert(), remove(), pop(), clear()
List Searching and Sorting: index(), count(), sort(), reverse()
List Copying and Nested Lists
List Comprehensions and Conditional Comprehensions
Tuples: Creation, Indexing, Slicing, Packing, and Unpacking
Tuple Methods: count() and index()
Sets: Creation, Uniqueness, and Set Operations
Set Methods and Mathematical Set Operations
Frozensets: Concepts and Operations
Dictionaries: Keys, Values, Items, and Methods
Dictionary Methods: get(), update(), pop(), setdefault(), and items()
Dictionary Iteration and Dictionary Comprehensions
Nested Collections and Collection Unpacking
Mutable vs. Immutable Objects
Collection Conversion and Common Collection Operations

Introduction to Functions and Modular Programming
Defining and Calling Functions Using def
Parameters, Arguments, and Function Calls
Positional, Keyword, and Default Arguments
Variable-Length Arguments: *args and **kwargs
Return Statements and Returning Multiple Values
Local and Global Variables
Variable Scope and the LEGB Rule
Lambda Functions
Recursive Functions and Recursion Examples
map(), filter(), and reduce() Functions
Docstrings and Function Annotations
Function Documentation Using __doc__ and help()
Practical and Real-Time Function Examples

Introduction to Modules and Packages
Built-in and User-Defined Modules
Importing Modules Using import and from...import
Module Aliases Using as
math Module: Mathematical Functions and Constants
random Module: Random Numbers, choice(), choices(), sample(), and shuffle()
time Module: Time Values, sleep(), and Basic Time Operations
datetime Module: date, time, datetime, and timedelta
Date and Time Formatting, Parsing, Comparison, and Calculations
Creating Custom Modules and Package Structures
__name__ == "__main__" and Script Execution
Installing and Managing Packages Using pip
Reusing Code Through Modules and Packages

Introduction to Errors and Exceptions
Syntax Errors vs. Runtime Exceptions
Common Python Exceptions: ValueError, TypeError, NameError, IndexError, KeyError, ZeroDivisionError
try and except Blocks
Handling Specific and Multiple Exceptions
else and finally Blocks
Raising Exceptions Using raise
Custom Exception Classes: Introduction
Exception Handling in Real-Time Programs

Introduction to File Handling
Opening Files Using open()
File Modes: r, w, a, x, b, and +
Reading Files Using read(), readline(), and readlines()
Writing and Appending Data to Text Files
File Object Methods: write(), writelines(), seek(), and tell()
Using the with Statement for Safe File Handling
CSV File Handling Using the csv Module
JSON File Handling Using the json Module
File and Directory Operations
Handling File-Related Exceptions

Common Built-in Functions: len(), type(), id(), and help()
Numeric Functions: abs(), round(), pow(), sum(), min(), and max()
Sorting and Reversing: sorted() and reversed()
Boolean Testing Functions: any() and all()
Iteration Helpers: enumerate() and zip()
Object and Sequence Functions: isinstance(), range(), and slice()
Practical Examples Using Built-in Functions

Nested Functions and Enclosing Scope
First-Class Functions and Functions as Objects
Passing Functions as Arguments
Higher-Order Functions
Returning Functions from Other Functions
Closures and Variable Scope
Decorators and @ Decorator Syntax
Practical Applications of Function Composition and Reuse

Introduction to Regular Expressions and the re Module
Regex Patterns and Raw Strings
Pattern Matching Using match(), search(), and fullmatch()
Finding Matches Using findall() and finditer()
Text Replacement Using sub() and subn()
Splitting Text Using split()
Special Sequences: \d, \D, \s, \S, \w, and \W
Word Boundaries and Anchors: \b, \B, \A, and \Z
Character Sets, Ranges, and Negation
Quantifiers, Groups, and Alternation
Positive and Negative Lookaheads
Password Validation Using Regular Expressions
Email, Phone Number, and Text Validation Examples

Classes, Objects, and Constructors (__init__)
Instance, Class, and Static Methods
Encapsulation, Inheritance, and Polymorphism
Method Overriding, Abstraction, and Abstract Classes
Magic or Dunder Methods

First-Class Functions and Nested Functions
Closures and Function Decorators
Decorators with Arguments & Multiple Decorators
Practical Logging and Timing Examples

Iterables vs. Iterators (iter() and next())
Creating Custom Iterators
Generator Functions and the yield Keyword
Generator Expressions & Lazy Evaluation

Patterns, Metacharacters, and Character Classes
match(), search(), findall(), and sub() functions
Groups, Lookaheads, Lookbehinds, and Word Boundaries
Email, Phone Number, and Password Validation

Comprehensions (List, Set, Dictionary)
Shallow Copy vs. Deep Copy
Context Managers, enumerate(), and zip()
Working with collections, itertools, and Type Hints

What is NumPy? & NumPy Arrays vs. Python Lists
Creating 1D and Multidimensional Arrays
Array Attributes (shape, size, ndim, dtype)

Indexing, Slicing, Reshaping, and Transposing Arrays
Joining, Splitting, Copying, and Viewing Arrays
Arithmetic Operations, Broadcasting, and ufuncs

Mathematical & Statistical Functions (Mean, Median, Std Dev, Variance)
Random Number Generation & Basic Matrix Operations
Linear Algebra Fundamentals

Series and DataFrames Creation
DataFrame Attributes, Methods, and Data Inspection

Reading and Writing CSV, Excel, and JSON Files
Setting and Resetting DataFrame Indexes

Using loc[] and iloc[] for Data Selection
Filtering, Sorting, Adding, and Renaming Columns
Vectorized Operations and Applying Functions

Identifying and Handling Missing Values
Removing Duplicates and Changing Data Types
String Operations and Handling Dates/Times

Aggregate Functions and groupby() Operations
pivot_table(), Merging, Joining, and Concatenating DataFrames

Importance of Visualization in Data Analysis
Introduction to pyplot, Figures, and Axes

Line, Bar, and Horizontal Bar Charts
Scatter Plots, Histograms, Pie Charts, and Box Plots

Titles, Axis Labels, Legends, and Grid Lines
Colors, Markers, Line Styles, and Annotations
Saving Charts as Image Files

Subplots, Multiple Axes, and Pandas DataFrame Plotting
Visualizing Trends and Handling Dates on Charts

Student Marks and Sales Trend Analysis
Product Comparisons and Salary Distribution Datasets

AI vs. Machine Learning vs. Deep Learning
Large Language Models (LLMs) & Token Context Windows
Applications, Limitations, and Ethical Usage

Writing Clear and Effective Prompts
Role-Based, Zero-Shot, and Few-Shot Prompting
Structured Output Generation and Refinement

ChatGPT, Google Gemini, and Microsoft Copilot Fundamentals
AI-Assisted Python Code Generation & Debugging
Generating SQL Queries and Testing Documentation

Linear algebra fundamentals: vectors, matrices and matrix operations
Matrix multiplication and determinants
Basic calculus, derivatives and gradients
Gradient descent fundamentals
Descriptive and inferential statistics
Population, samples and sampling methods
Mean, median, mode, variance and standard deviation
Quartiles, percentiles and interquartile range
Probability rules and conditional probability
Bayes' theorem
Normal, binomial and Poisson distributions
Central Limit Theorem
Covariance, correlation, skewness and kurtosis
Confidence intervals and hypothesis testing
Z-test, t-test, chi-square test and ANOVA fundamentals

Relational database fundamentals and database setup
Database, table and data-type creation
DDL, DML, DQL, DCL and TCL
Primary keys, foreign keys and constraints
SELECT, WHERE, ORDER BY and DISTINCT
Filtering using LIKE, IN, BETWEEN and NULL
Aggregate functions, GROUP BY and HAVING
Inner, left, right and cross joins
Subqueries and correlated subqueries
String and date functions
Common Table Expressions (CTEs)
Window functions: ROW_NUMBER(), RANK(), DENSE_RANK() and LAG()
Views, indexes and stored procedures
Transactions and ACID properties
Connecting SQL databases with Python
Basic query optimization

Understanding business problems and datasets
Data sourcing and data-quality checks
Data cleaning and preprocessing
Handling missing values and duplicate records
Identifying and treating outliers
Univariate, bivariate and multivariate analysis
Data distributions and correlation analysis
Categorical and numerical data analysis
Feature creation and derived metrics
Encoding categorical variables
Normalization and standardization
Exploratory reports and insight generation

Importing Excel, CSV and SQL data
Data transformation using Power Query
Data modeling and relationships
Introduction to star-schema modeling
DAX calculated columns and measures
Common aggregation and time-intelligence functions
KPIs, charts, slicers and interactive dashboards
Report publishing and sharing
Presenting business insights effectively

Introduction to Artificial Intelligence and Machine Learning
Supervised, unsupervised and reinforcement learning concepts
ML workflow and problem definition
Training, validation and test datasets
Data preprocessing and feature engineering
Linear and multiple linear regression
Polynomial regression
Logistic regression
K-Nearest Neighbors (KNN)
Model evaluation fundamentals
Overfitting, underfitting and generalization

Decision trees and random forests
Support Vector Machines (SVM)
Naive Bayes
Ridge and Lasso regression
Ensemble learning, bagging and boosting
Introduction to XGBoost
K-Means clustering
Hierarchical clustering and DBSCAN fundamentals
Principal Component Analysis (PCA)
Feature selection and dimensionality reduction fundamentals

Confusion matrix
Accuracy, precision, recall and F1-score
ROC curves and AUC
Regression metrics: MAE, MSE, RMSE and R-squared
Cross-validation
GridSearchCV and RandomizedSearchCV
Regularization and model pipelines
Data leakage prevention
Model comparison, error analysis and model improvement

Introduction to time series data
Trend, seasonality and residuals
Time-based visualization and decomposition
Stationarity fundamentals
Autocorrelation and partial autocorrelation
ARIMA introduction
Introduction to Prophet
Forecast evaluation metrics
Sales and demand forecasting applications

Introduction to neural networks
Perceptrons and feedforward networks
Activation functions
Forward propagation and backpropagation
Loss functions and optimizers
Gradient descent fundamentals
TensorFlow and PyTorch introduction
Tensors and model training
Batch normalization and dropout
Saving, loading and evaluating models

Image representation and preprocessing
Convolution, filters and feature maps
Pooling layers and CNN architecture
Image augmentation
Image classification
Introduction to object detection and segmentation
Transfer learning and fine-tuning
Using pretrained CNN models
Model evaluation and error analysis

Image loading, saving and manipulation
Image resizing, cropping and transformations
Color spaces and thresholding
Edge and contour detection
Feature extraction fundamentals
Introduction to object detection
Optical Character Recognition (OCR) fundamentals

Text cleaning and normalization
Tokenization and stop-word removal
Stemming and lemmatization
N-grams and text preprocessing
Bag of Words and TF-IDF
Part-of-speech tagging
Named Entity Recognition (NER)
Word embeddings
Word2Vec, CBOW and Skip-Gram fundamentals
Cosine similarity
Text classification and sentiment analysis
Text similarity and summarization

Introduction to transformer architecture
Attention and self-attention
Multi-head attention fundamentals
Encoder, decoder and encoder-decoder architectures
Embeddings and positional encoding
BERT and GPT model families
Tokenization and context windows
Prompt structures and structured outputs
Applications of transformers in NLP and Generative AI

Introduction to Generative AI and LLMs
GPT, Llama and Mistral model families
Working with ChatGPT, Microsoft Copilot, Gemini and Grok
Zero-shot, one-shot and few-shot prompting
Instruction design and prompt templates
Structured JSON outputs
Working with LLM APIs
Introduction to local LLMs using Ollama
Hallucinations, privacy and responsible AI

RAG architecture and workflow
Document loading and text extraction
Document chunking and metadata
Embeddings and vector representations
Vector databases and indexing
Semantic search and retrieval
Context construction and answer generation
LangChain fundamentals
Multi-PDF question-answering systems
Web-content retrieval
RAG versus fine-tuning
Introduction to managed RAG services
Retrieval quality and source references

Fine-tuning concepts and use cases
When to use prompting, RAG or fine-tuning
Dataset preparation
Parameter-efficient fine-tuning overview
Introduction to LoRA and QLoRA
Quantization concepts
Training and validation considerations
Advanced fine-tuning implementation may be offered as an optional extension

Evaluating LLM outputs
BLEU and ROUGE fundamentals
Perplexity concepts
RAGAS introduction
Retrieval relevance and answer quality
Faithfulness and hallucination analysis
Test datasets and evaluation reports

Agent architecture and workflow
Tool calling and function calling
Connecting LLMs with external tools
LangChain tools
LangGraph fundamentals
State management and multi-step workflows
Introduction to n8n automation
Introduction to Model Context Protocol (MCP)
Error handling and human approval workflows
Building practical AI assistants

Introduction to Flask
REST API development
FastAPI fundamentals
Streamlit applications and dashboards
Integrating ML models into applications
Connecting LLM APIs and RAG pipelines
Input validation and error handling
Testing and debugging
Git and GitHub
Project documentation and version control

Introduction to cloud computing
AWS fundamentals
EC2 and application deployment concepts
Server setup and configuration
Environment variables and application security basics
ETL: Extract, Transform and Load
Data pipelines and data-quality checks
Introduction to data warehouses and data lakes
Connecting data workflows with ML applications

Week 1 — Planning and Data Preparation: define the business problem, select the dataset, clean data, perform EDA and create a project plan
Week 2 — Development: build the ML model, AI application or RAG workflow and implement the main functionality
Week 3 — Testing and Deployment: evaluate results, fix errors, improve performance and deploy a suitable application
Week 4 — Final Submission and Assessment: complete documentation, prepare the presentation, demonstrate the application and attend a mentor review
Project options: customer churn prediction, house-price prediction, sales forecasting, customer segmentation, image classification, AI-powered EDA dashboard, AI career coach, Multi-PDF RAG chatbot, Text-to-SQL assistant, AI business assistant or website chatbot
Deliverables: source code and GitHub repository, dataset details, evaluation/testing results, working demonstration, technical documentation, project report and final presentation
Internship policy: guided project-based internship; external-company placement, stipend and certificate conditions should only be advertised when confirmed by SivaSoft

Python programming interview questions
DSA coding problems and complexity analysis
SQL queries and database concepts
Statistics and probability interview questions
Machine learning algorithms and evaluation
Deep learning and NLP fundamentals
Transformers, LLMs and RAG concepts
Agentic AI architecture fundamentals
Project explanation and technical presentation
Resume and GitHub portfolio preparation
Mock interviews and coding assessments
Career guidance and placement assistance according to SivaSoft's applicable policy

Python and DSA coding exercises
SQL sales analysis and Power BI dashboard
EDA on a real-world dataset
House-price prediction and spam-email classification
Breast cancer prediction and customer churn prediction
Customer segmentation and demand forecasting
Image classification and sentiment analysis
Text summarization and AI career coach
Multi-PDF RAG chatbot and Text-to-SQL assistant
AI business assistant or website customer-support chatbot

Fee Structure & Batches

Group Online Fee You Save ₹15,000

₹15,000 ~~₹30,000~~

Classroom Fee You Save ₹25,000

₹25,000 ~~₹50,000~~

One-to-One Online You Save ₹75,000

₹75,000 ~~₹1,50,000~~

Syllabus and certificates are 100% identical across all batch options. Only the batch mode and pricing differ.

Book Free Demo Class Call +91 99636 89248

On Reviews — What Our Students Say

★★★★★

"I had a great learning experience at SivaSoft. Siva Sir explains Python, SQL, Data Science, and Machine Learning concepts clearly with practical examples. His industry knowledge and step-by-step teaching approach made even complex topics easier to understand. The sessions on Generative AI, RAG, and Agentic AI were especially interesting. I highly recommend this training for anyone looking to build a career in Data Science."