Mastering a Full Stack Python with Generative AI – Secure Your Dream Job

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From the very first day, we ensure our candidates receive unlimited placement calls, giving them maximum opportunities to connect, interview, and secure their dream roles with our hiring partners.

We build your technical foundation, enhance your practical skills, and prepare you for real-world interviews through:

💡 Live Project Experience
💼 Mock Interviews with Experts
🧠 Aptitude & Soft Skills Training
📈 Career Counseling and Resume Building

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Comprehensive Full Stack Python With Genrative AI Syllabus.

Designed for Industry Readiness with Practical, Hands-on Implementation

Full Stack Python Developer Course in Pune – By Advanto Infotech with 100 % Unlimited placement Calls

Looking for the best Python training in Pune with 100% Unlimited placement calls Look no further than Advanto Infotech – Pune’s most trusted and recognized training institute. We offer top-notch, industry-oriented Python courses delivered by expert trainers with over 10+ years of real-time experience.

Our Python training is fully based on live projects to ensure hands-on learning. Whether you’re a student, fresher, or working professional, our affordable Python course fees and flexible batches make it easy to upskill. Upon successful completion, you’ll also receive a Python certification to boost your resume

Why Choose Advanto for Python Training in Pune?

Get trained by one of the most experienced and trusted Python trainers in Pune through a comprehensive, 100% live project-based learning approach designed to build real-world skills. Our program offers dedicated placement support with unlimited interview calls until you get placed, along with personalized guidance to help you confidently crack interviews. Whether you’re a student or a working professional looking to upskill, our affordable fee structure ensures high-quality training is accessible to all. Take the first step toward a successful IT career with our hands-on, career-focused Python training.

Eligibility Criteria

Graduates from BE/B.Tech, M.Sc, MCA, M.E/M.Tech, BCs, BCA.

Final year students awaiting results are also eligible to apply.

At Advanto Infotech, we are committed to your success. With personalized mentorship, live projects, and robust placement support, your career in Python or Full Stack Development starts here.

100% Placement Assistance

Advanto Infotech is one of Pune’s most trusted Python Full Stack Developer training institutes with a proven track record. We provide 100% placement assistance, including:

Unlimited placement calls
Personalized interview preparation
Resume building and mock sessions

With over 8 years of training experience, we’ve successfully placed hundreds of students in top MNCs and IT companies. Completing this course gives you a near-guaranteed pathway to employment in the tech industry.

Full Stack Python With Generative AI Course Content.

Designed for Industry Readiness with Practical, Hands-on Implementation

What is this course all about?

his course is a comprehensive AI-Powered Full Stack Development program designed to transform traditional developers into “10x Developers” by integrating cutting-edge Generative AI and automation into the software lifecycle. It bridges the gap between core engineering and the future of intelligent applications.

ANN, CNN, and RNN.

Difference between ANN, CNN, and RNN. What is LLM and how it is related to RNN?

Generative AI Basic concepts.

Difference between Gen AI and Agentic AI.

Gen AI tools and applications.

Notebook LM, Gemini, ChatGPT, Claud, Grok, Multi,
DeepSeek, Write-Sonic, Meta, Perplexity

Creating a complete PowerPoint

presentation using Gamma.ai.

Converting Video

Converting Video to text using Google AI Studio.

AI agent

Creating an AI agent using Gemini gems.

UI/UX design

Creating basic UI/UX design using Google Gemini nano banana model. Convert this design
into a website.

Text summary

Creating text summary using marlin.ai

infographics

Creating professional infographics using napkin.ai

Creating dashboard

Analyzing data and creating a dashboard using Julius.ai,

Analyzing data

Analyzing data using an AI tool from scratch

Excel formulas

Create complex Excel formulas using AI tools.

Prompt engineering

scratch.
13. Create complex Excel formulas using AI tools.
14. P

data science project

Create a complete data science project using Chat GPT and Copilot from scratch.

Creating content

Creating content using ai tools.

Thumbnails for your YouTube

Creating thumbnails for your YouTube videos using an AI tool.

Creating a website

using lovable.

professional presentations

Creating professional presentations using Gen Spark.

Application

Creating your own application using Claud.

Notebook LM

Notebook LM for content creation

Sonic

What is Write Sonic and how we can use it.

Creating a banner

Creating a banner for a travel company using ai tools.

Creating video

Creating a video using AI tools
Creating videos on stories based on ancient literature using AI

Data creation and data cleaning

Data creation and data cleaning for data analysis using ai tools.

Digital marketing

AI tools for digital marketing

Creating the image.

Leonardo for creating the image.

Create music.

Suno to create music.

Power BI using an AI tool.

Creating a professional dashboard in Power BI using an AI tool.

Data visualization

Creating data visualization and reports in Tableau using an AI tool.

Building an LLM from scratch

LLM Basics
2. Presenting vs finetuning
3. What are transformers
4. How does GPT-3 work?
5. Stages of building an LLM
6. How to code an LLM tokenizer?
7. Byte pair encoding.
8. Creating input-target pairs.
9. Token embeddings
10. Positional embeddings
11. Entire data processing pipeline
12. What do you mean by attention?
13. Python code example.
14. Key, Query, Value: Code the self-attention.
15. Casual Self-attention mechanism.
16. Multi head attention mechanism.
17. LLM architecture.
18. Layer Normalization.
19. GELU Activation function.
20. Shortcut connections.
21. Transformer Block.
22. 124 million parameters GPT-2 model.
23. Coding GPT-2 to predict the next token.
24. Measuring LLM loss function.
25. Data set demo.
26. LLM pretraining loop.
27. LLM decoding strategies- temperature scaling.
28. Top k sampling in LLM
29. Saving and loading LLM model weights.
30. LLM fine-tuning.
31. LLM classification finetuning.
32. Datasets and data loaders.
33. Coding model architecture.
34. Spam classification
35. Alpha prompt datasets
36. Data batching.
37. Data loaders.
38. Loading pre-training LLM
39. Fine-tuning training loop
40. OLLAMA evaluation

AI automation.

Introduction to AI automation.

n8n

Introduction to n8n.

Project

Complete automation using n8n.

HTML

Introduction to HTML
Basic Structure of HTML
HTML Editors
HTML Tags
Paragraphs, Headings and Text
Formatting Tags
HTML Lists
HTML Images
HTML Tables
HTML Forms
HTML Media

CSS (Cascading Style Sheets)

Introduction to CSS
Types of CSS
CSS Properties, Selectors and Values
Applying CSS to HTML
Paragraphs, Headings and Text
CSS Box Model, Margins, Padding, Borders
CSS Text & Font Formats
CSS Advanced Topics (Effects, Animations, Shadows, Etc..

Javascript

Introduction to JavaScript
How to Apply JavaScript
Displaying Output in JavaScript
Understanding JavaScript Syntax
Variables & Datatypes
Operators
Math and String Manipulations
Conditional and looping Statements
Functions
Validations
Events

Bootstrap

Introduction to Bootstrap
Bootstrap Setup
Bootstrap Containers
Bootstrap Grids
Bootstrap Tables
Bootstrap Buttons, Navbars, Alerts
Bootstrap Carousel
Bootstrap Forms

Angular

Introduction to Angular
Environment Setup
Installing Angular CLI
Directory Structure of Angular
Angular Fundamentals
Angular Building Blocks
Angular Data Binding
String Interpolation
Directives and Pipes
Forms
Approaches (Driven & Reactive)
Validators
Routing

Pyhton

Introduction to python
What is Python and history of Python?
Unique features of Python
Python-2 and Python-3 differences
Install Python and Environment Setup
First Python Program
Python Identifiers, Keywords and Indentation
Comments and document interlude in Python
Command line arguments
Getting User Input
Python Data Types
What are variables?
Python Core objects and Functions
Number and Maths
Assignments

Control Flow Statements

if-else
if-elseif-else
while loop
for loop
break
continue
assert
pass
return

List, Ranges & Tuples in Python

Introduction
Lists in Python
More about Lists
Understanding Iterators
Generators, Comprehensions and Lambda Expressions
Generators and Yield
Next and Ranges
Understanding and using Ranges
More About Ranges
Ordered Sets with tuples

Python Dictionaries and Sets

Introduction to the section
Python Dictionaries
More on Dictionaries
Sets
Python Sets Examples

Input and Output in Python

Reading and writing text files
writing Text Files
Appending to Files and Challenge
Writing Binary Files Manually
Using Pickle to Write Binary Files

Python function

Python user defined functions
Python packages functions
Defining and calling Function
The anonymous Functions
Loops and statement in Python
Python Modules & Packages

Python Object Oriented

Overview of OOP
The self variable
Constructor
Types Of Variables
Namespaces
Creating Classes and Objects
Inheritance
Types of Methods
Instance Methods
Static Methods
Class Methods
Accessing attributes
Built-In Class Attributes
Destroying Objects
Abstract classes and Interfaces
Abstract Methods and Abstract class
Interface in Python
Abstract classes and Interfaces

Exceptions

Errors in Python
Compile-Time Errors
Runtime Errors
Logical Errors
What is Exception?
Handling an exception
try….except…else
try-finally clause
Argument of an Exception
Python Standard Exceptions
Raising an exceptions
User-Defined Exceptions

Python Regular Expressions

What are regular expressions?
The match Function
The search Function
Matching vs searching
Search and Replace
Extended Regular Expressions
Wildcard

Python Multithreaded Programming

What is multithreading?
Difference between a Process and Thread
Concurrent Programming and GIL
Uses of Thread
Starting a New Thread
The Threading Module
Thread Synchronization
Locks
Semaphore
Deadlock of Threads
Avoiding Deadlocks
Daemon Threads

Using Databases in Python

Python MySQL Database Access
Install the MySQLdb and other Packages
Create Database Connection
CREATE, INSERT, READ Operation
DML and DDL Oepration with Databases
Web Scraping in Python

Data Science Using Python

Numpy:
Introduction to numpy
Creating arrays
Indexing Arrays
Array Transposition
Universal Array Function
Array Processing
Array Input and Output
Matplotlib: Data Visualization
Python for Data Visualization
Welcome to the Data Visualization Section
Introduction to Matplotlib

Machine Learning

Machine Learning Introduction
Machine Learning Algorithms
Linear Regression and Logistic Regression
Decision Tree

Graphical User Interface

GUI in Python
Button Widget
Label Widget
Text Widget

Django Web Framework in Python

Django overview
Creating a project
Apps life cycle
Admin interface
Creating views
URL Mapping
Template system
Models
Form details
Testing
Page redirection
Sending Emails
Deploying Django framework
Form processing
File uploading
Cookie handling
Sessions, caching and comments
RSS,AJAX
Sending Emails

Database

Database Handling with MySQL
Python MySQL Database Access
Create Database Connection
DML and DDL Operations with Databases
Performing Transactions
Handling Database Errors
Disconnecting Database
Database Handling with MongoDB
SQL vs NoSQL
MongoDB
PyMongo
Establishing a Connection
Accessing Database
DML and DDL Operations
Oracle
MySql
MongoDB