Python for AI Bootcamp 2026: 15-Day Complete Syllabus from Python Basics to Generative AI
Artificial Intelligence is transforming the way we work, learn, and build software. From Machine Learning and Deep Learning to Generative AI and AI Agents, Python has become one of the most important programming languages for developing modern AI applications.
If you want to start your journey in Artificial Intelligence but don't know where to begin, this 15-Day Python for AI Bootcamp is designed for you.
This bootcamp starts with Python programming fundamentals and gradually takes you through data analysis, Machine Learning, Deep Learning, Large Language Models (LLMs), Generative AI, and AI Agents.
By the end of this bootcamp, you will have practical experience building AI-powered applications using Python.
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Python for AI Bootcamp 2026 – Overview
Bootcamp Name: Python for AI – 15 Days Bootcamp
Duration: 15 Days
Level: Beginner to Advanced
Programming Language: Python
Learning Mode: Practical, Hands-on Learning
Prerequisites: No prior Python programming experience required
Final Goal: Build a real-world AI application using Python
What Will You Learn?
During these 15 days, you will explore six major learning phases:
Python Programming Fundamentals
Advanced Python and Essential AI Libraries
Mathematics and Machine Learning
Deep Learning and Neural Networks
Generative AI, LLMs, RAG and AI Agents
Final Real-World AI Project
Complete 15-Day Python for AI Bootcamp Syllabus
PHASE 1 – Python Programming Foundations (Day 1–4)
The first phase focuses on building a strong foundation in Python programming. You will learn essential programming concepts and develop the ability to write Python programs independently.
Day 1: Introduction to Python Programming
Topics Covered:
Introduction to Python and its applications
Why Python is popular in AI and Machine Learning
Installing Python and setting up Jupyter Notebook
Python syntax and indentation
Variables and constants
Data types in Python
Type conversion
Input and output operations
Basic Python programs
Practical Exercises:
Print statements
Simple calculator
User input programs
Basic mathematical operations
Day 2: Python Operators and Control Flow
Topics Covered:
Arithmetic operators
Comparison operators
Logical operators
Assignment operators
Membership operators
Identity operators
Conditional statements
if, elif, and else
Nested if statements
for and while loops
break, continue, and pass
Practical Exercises:
Even and odd number checker
Number guessing game
Multiplication table generator
Simple grading system
Day 3: Python Data Structures
Topics Covered:
Introduction to Python data structures
Lists and list methods
Tuples and tuple operations
Sets and set operations
Dictionaries and dictionary methods
Indexing and slicing
Nested data structures
List, dictionary, and set comprehensions
Practical Exercises:
Student marks management
Remove duplicate elements
Dictionary-based data management
Data filtering programs
Day 4: Python Functions and Advanced Basics
Topics Covered:
Introduction to functions
Creating and calling functions
Function arguments and return values
Default and keyword arguments
*args and **kwargs
Lambda functions
map(), filter(), and zip()
Modules and packages
Importing Python libraries
Practical Exercises:
Reusable Python functions
Data filtering using lambda
Creating custom modules
Mini project using functions
PHASE 2 – Advanced Python and Libraries for AI (Day 5–6)
Before entering Machine Learning, participants will learn how to work with real-world data and important Python libraries.
Day 5: Advanced Python Programming
Topics Covered:
Exception handling
try, except, else, and finally
File handling
Reading and writing CSV and text files
Object-Oriented Programming
Classes and objects
Constructors
Inheritance
Encapsulation
Introduction to Python virtual environments
Practical Exercises:
File management application
Exception handling programs
Student management system using classes
Day 6: Python Libraries for AI and Data Science
Topics Covered:
NumPy
Introduction to NumPy
Creating arrays
Array indexing and slicing
Array operations
Broadcasting
Basic statistical operations
Pandas
Introduction to Pandas
Series and DataFrames
Reading CSV files
Data selection and filtering
Handling missing values
Data cleaning
Data Visualization
Introduction to Matplotlib
Introduction to Seaborn
Creating charts and graphs
Understanding data distributions
Practical Exercises:
Analyze a CSV dataset
Clean missing data
Perform numerical operations
Create data visualizations
PHASE 3 – Mathematics and Machine Learning (Day 7–9)
This phase introduces the mathematical concepts and Machine Learning algorithms behind modern Artificial Intelligence systems.
Day 7: Mathematics for Artificial Intelligence
Topics Covered:
Introduction to mathematics for AI
Linear algebra fundamentals
Scalars, vectors, and matrices
Matrix operations
Probability fundamentals
Mean, median, and mode
Variance and standard deviation
Basic statistics
Understanding data distributions
Practical Exercises:
Statistical analysis using NumPy
Matrix operations
Probability calculations
Statistical data visualization
Day 8: Introduction to Machine Learning
Topics Covered:
What is Artificial Intelligence?
AI vs Machine Learning vs Deep Learning
What is Machine Learning?
Applications of Machine Learning
Supervised Learning
Unsupervised Learning
Classification and Regression
Training and testing datasets
Introduction to Scikit-learn
Model training workflow
Practical Exercises:
Build your first Machine Learning model
Split datasets into training and testing sets
Train a basic predictive model
Day 9: Machine Learning Algorithms
Topics Covered:
Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors (KNN)
Model training and prediction
Model evaluation
Accuracy, precision, and recall
Confusion matrix
Overfitting and underfitting
Practical Projects:
House price prediction
Student performance prediction
Customer classification
Basic predictive analytics
PHASE 4 – Deep Learning and Neural Networks (Day 10–11)
Deep Learning is an important part of modern AI. In this phase, you will understand how neural networks work and learn to build basic Deep Learning models.
Day 10: Introduction to Deep Learning
Topics Covered:
What is Deep Learning?
Machine Learning vs Deep Learning
Introduction to neural networks
Biological neurons vs artificial neurons
Neural network architecture
Input, hidden, and output layers
Activation functions
Forward propagation
Backpropagation
Loss functions
Gradient descent
Practical Exercises:
Understand neural network architecture
Implement basic mathematical operations behind neural networks
Explore activation functions
Day 11: Deep Learning with Python
Topics Covered:
Introduction to PyTorch or TensorFlow
Understanding tensors
Creating tensors
Tensor operations
Building neural networks
Model training
Loss functions and optimizers
Model evaluation
Introduction to image classification
Practical Projects:
Build a basic neural network
Handwritten digit recognition
Introduction to image classification
PHASE 5 – Generative AI, LLMs, RAG and AI Agents (Day 12–14)
This is one of the most exciting phases of the bootcamp. You will explore modern Generative AI technologies and learn how to build practical AI-powered applications using Python.
Day 12: Introduction to Generative AI and Large Language Models
Topics Covered:
What is Generative AI?
Traditional AI vs Generative AI
Introduction to Large Language Models (LLMs)
How LLMs work
Introduction to Transformer architecture
Tokens and tokenization
Embeddings
Attention mechanism
Introduction to Hugging Face
Overview of popular LLMs
Practical Exercises:
Explore pre-trained AI models
Generate text using an LLM
Understand tokenization and embeddings
Day 13: Building Generative AI Applications with Python
Topics Covered:
Introduction to LLM APIs
Connecting Python with AI models
Prompt engineering fundamentals
System prompts and user prompts
Zero-shot and few-shot prompting
Structured outputs
Function calling and tool use
Building AI chatbots
Understanding API keys and environment variables
Practical Projects:
Build an AI chatbot
Create a text summarizer
Develop an AI-powered content assistant
Day 14: RAG and AI Agents with Python
Topics Covered:
Introduction to Retrieval-Augmented Generation (RAG)
Why RAG is important
Document loading and processing
Text chunking
Embeddings and vector databases
Introduction to FAISS and ChromaDB
Semantic search
Introduction to LangChain
Understanding AI Agents
Tool calling and agent workflows
Practical Projects:
Build a PDF question-answering application
Create a document-based AI assistant
Build a basic AI agent using tools
PHASE 6 – Final Capstone Project (Day 15)
Day 15: Build a Real-World AI Application
On the final day, participants will apply the concepts learned throughout the bootcamp to develop a practical AI application.
Choose one of the following capstone projects:
1. AI PDF Assistant
Build an application that allows users to upload PDF documents and ask questions about their content.
2. AI Resume Analyzer
Create an AI application that analyzes resumes, extracts skills, and provides improvement suggestions.
3. AI Customer Support Chatbot
Develop an intelligent chatbot that answers customer questions using an LLM.
4. AI Research Assistant
Build an application that summarizes research papers and answers questions about documents.
5. AI Data Analyst
Create an AI-powered application that allows users to ask questions about datasets using natural language.
Final Activities:
Project development
Testing and debugging
Understanding deployment fundamentals
Project demonstration
Discussion of real-world AI applications
Future learning roadmap
Technologies and Python Libraries Covered
| Category | Tools and Technologies |
|---|---|
| Programming | Python, Jupyter Notebook |
| Numerical Computing | NumPy |
| Data Analysis | Pandas |
| Data Visualization | Matplotlib, Seaborn |
| Machine Learning | Scikit-learn |
| Deep Learning | PyTorch / TensorFlow |
| Generative AI | LLM APIs, Hugging Face |
| AI Frameworks | LangChain |
| Vector Databases | FAISS, ChromaDB |
| Development | Git, GitHub, Streamlit |
What Will You Be Able to Build After 15 Days?
After completing this bootcamp, you should have a foundation to:
Write Python programs confidently.
Work with real-world datasets.
Perform data analysis using Pandas and NumPy.
Understand fundamental Machine Learning algorithms.
Build basic predictive models.
Understand neural networks and Deep Learning.
Work with Large Language Models.
Develop AI chatbots using Python.
Build document-based AI applications using RAG.
Understand the fundamentals of AI Agents.
Develop a practical AI project for your portfolio.
Who Should Join This Bootcamp?
This bootcamp is suitable for:
Beginners who want to learn Python from scratch.
College students interested in Artificial Intelligence.
Python developers who want to enter AI development.
Data Science and Machine Learning enthusiasts.
Software developers interested in Generative AI.
Anyone who wants to build AI-powered applications.
No previous Python experience is required. We will start with the fundamentals and gradually move towards practical AI development.
Final Thoughts
Artificial Intelligence is creating exciting opportunities for developers, students, and technology enthusiasts.
Learning Python is an excellent starting point for entering the world of AI. However, understanding the fundamentals and building practical projects are equally important.
This 15-day Python for AI Bootcamp is structured to take you from basic Python programming to building your first Generative AI application.
The goal is not just to learn Python syntax or understand AI concepts. The goal is to develop the skills required to start building real-world AI applications.
Start with Python. Learn Machine Learning. Explore Deep Learning. Build with Generative AI.
Organized by CLCODING
Website: https://www.clcoding.com/
Learn. Code. Build. Repeat.
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