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Become an AI & Machine Learning Expert with Python!

Master Artificial Intelligence, Machine Learning, Deep Learning & Python with hands-on projects, real-world datasets, and industry-ready skills designed by experts. Join thousands of learners accelerating their careers in AI & data science.

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Become an AI & Machine Learning Expert with Python!

Master Artificial Intelligence, Machine Learning, Deep Learning & Python with hands-on projects, real-world datasets, and industry-ready skills designed by experts. Join thousands of learners accelerating their careers in AI & data science.

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AI & Machine Learning with Python Course Overview

The Complete AI & Machine Learning with Python Bootcamp offers a structured, industry-ready learning path to help you master in-demand AI skills. Learn Machine Learning, Deep Learning, Data Science, NLP, and Python through real-world projects and hands-on training designed by experts.

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AI & Machine Learning with Python Curriculum

What You Will Learn in This AI & ML Bootcamp

Python Zero to Hero

4 Modules | 21 Hours

Mastering Databases with Python

4 Modules | 4 Hours

DSA using Python

4 Modules | 20 Hours

Building a Chatbot Using Python

4 Modules | 2 Hours

AI Fundamentals

4 Modules | 4 Hours

Introduction to Machine Learning

4 Modules | 4 Hours

Scikit-Learn for Machine Learning Part-1

4 Modules | 4 Hours

Python SciPy

4 Modules | 4 Hours

NLTK

4 Modules | 4 Hours

Computer vision project

4 Modules | 4 Hours

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Each time you complete a course in the Data Analyst Bundle, you’ll receive a professional certificate issued by HCL GUVI (an IIT-M & IIM-A incubated EdTech company). All certificates are ISO-certified, globally recognized, and shareable on LinkedIn and resumes, helping you showcase your skills, boost your profile, and stand out to recruiters.

Earn Industry-Recognized Certificates

Each time you complete a course in the Data Analyst Bundle, you’ll receive a professional certificate issued by HCL GUVI (an IIT-M & IIM-A incubated EdTech company). All certificates are ISO-certified, globally recognized, and shareable on LinkedIn and resumes, helping you showcase your skills, boost your profile, and stand out to recruiters.

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Every certificate reflects practical knowledge and tools you’ve mastered — evidence of real, job-ready skills.

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Earn Industry-Recognized AI & Machine Learning Certificates

Each time you complete a course in the AI & Machine Learning with Python Bootcamp, you’ll earn a professional certificate issued by GUVI (an IIT-M & IIM-A incubated EdTech company). These certifications are globally recognized and designed to elevate your career in AI, Data Science, and emerging technologies.

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Bonus Features You Get

CodeKata

An interactive coding platform with 1500+ curated problems by IT experts. CodeKata tracks your progress and builds a coding profile to highlight your skills for job opportunities.

WebKata

WebKata is a developing practice platform for beginners, built with top-notch web development problem statements focusing on Modern HTML, CSS, and JavaScript. 

SQLKata

SQLKata is a hands-on tool to master SQL through concept-driven exercises. It builds skills in query writing, optimization, and real-world database management.

Debugging

Debugging a series of programs curated by industry experts. Practicing debugging will help you get started and become familiar with programming. 

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Frequently Asked Questions

What do I actually get with this bootcamp?

You’ll get complete access to the AI & Machine Learning with Python Bootcamp, covering Python programming, Data Analysis, Machine Learning, Deep Learning, NLP, and Computer Vision. Each module includes hands-on projects, real-world datasets, expert support, and certificates upon completion.

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No. This is a one-time payment of ₹1499 with no subscriptions or hidden charges.

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You get lifetime access, so you can learn anytime at your own pace.

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Are the lessons live or pre-recorded?

All lessons are pre-recorded, allowing you to learn at your convenience.

Will I get to practice what I learn?

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Python Zero-to-Hero
Beginner Module
  • Introduction to Python Zero to Hero Course
  • Introduction to Python & Features of Python
  • PVM, Frozen Binaries & Memory management
  • Execution & Viewing the Byte Code
  • Installing & Testing Python for Windows
  • Setting the Path & Executing First Python Program
  • Comments in Python & Docstrings
  • Datatypes & Built-in datatypes
  • Bool Datatype, Sequences in Python & Sets
  • Variable & Determining the datatype
  • Variables Rules & Conventions
  • User-defined Datatypes & Constants
  • Operators
  • Control Statements
  • The if Statement
  • Indentation & Short Hand if
  • Iteration_Control_Flow
  • The while Loop
  • For Loop - break and continue statements
  • Infinite & Nested Loops
  • Defining & Calling a Function
  • Returning Values from a Function
  • Formal, Actual & Positional Arguments
  • Keyword & Default Arguments
  • Variable Length Arguments & Recursive function
  • Assignment 1
  • Project: Python Escape Room Game Part-1
  • Project: Python Escape Room Game Part-2
Intermediate Module
  • Anonymous Functions or Lambdas
  • Generators & Decorators
  • Structured Programming & modules
  • Input, Filter and Map
  • Strings - Creating & Length of a String
  • Indexing in Strings
  • Slicing & Reverse Strings
  • Case Modification
  • Membership, Replacement & Sub Strings
  • Split, Join, Find, Index
  • Concatenation of Strings
  • Lists- Create
  • Lists - Slicing and Updating
  • Lists - Add and Remove Items
  • List Remaining Methods
  • Membership & Nested Lists
  • List Comprehensions
  • Tuples Creating and Modifications
  • Basic Operations on Tuples
  • Sets
  • Operations on Dictionaries
  • Dictionary Methods
  • Looping through and sorting dictionaries
  • Assignment 2
  • Project: World Wizard Part-1
  • Project: Word Wizard Part-2
Advanced Module
  • OOPS Concepts & Features
  • Constructor, Variables & Namespace
  • Method types, Passing member & Inner Classes
  • Constructors in Inheritance
  • Overriding Constructors, Inheritance, Super()
  • Polymorphism in Python
  • Errors and Exception, Handling, etc
  • Assignment 3
  • Project: Gladiator Game Part-1
  • Project: Gladiator Game Part-2
Expert Module
  • Files in Python
  • Pickle and With Statement
  • Random Accessing & Zipping
  • Regular Expressions
  • Quantifiers in Regular Expressions
  • Special Characters & Sequences
  • Multithreading & Concurrent Programming
  • Thread Class Methods
  • Thread Synchronization(Lock and Semaphore)
  • Deadlocks and Avoiding Deadlock
  • Thread Communications
  • Assignment 4
  • Project: Secret Agent Part-1
  • Project: Secret Agent Part-2
Mastering Databases with Python 
Beginner Module
  • Introduction
  • Introduction, Relational, Non Relational, SQL vs NoSQL
  • Tables, Record and Fields
  • Data exploration & Core Insights
  • Joins & Advanced Analytical Scenarios
  • Installing MySQL: PostgreSQL and Required python Libraries
Intermediate Module
  • Stored Procedures , Views & Performances
  • Introduction to ORMs and SQLAlchemy
  • Relationship, Joins & Migrations
  • NoSQL with MongoDB and Python
  • Advanced MongoDB Operations
Advanced Module
  • Real World Project with Python and Databases
  • Testing, Debugging, Visualization & Deployment
  • Data Analysis , ETL and Cloud Integration
Expert Module
  • Asynchronous Access, Security, Backups & Logging
  • Writing Clean and maintainable Code
  • Managing Connection & Common Pitfalls
DSA using Python
Beginner Module
  • What are algorithms and data structures?
  • Asymptotic Big O, Big Omega , Big Theta notation
  • Algorithm Analysis
  • Best, Worst & Average Case Time Complexity
  • Data structure design principles
  • Arrays
  • Coding Example with arrays
  • Stacks
  • Linked lists (Single Linked List)
  • Delete Operations in Linked List
  • Circular Linked List
  • Double Linked List
  • Coding Example with linked list
  • Queues
  • Coding example with queue
  • Circular Queue
  • Double Ended Queue
  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • merge Sort
  • Quick Sort
  • Coding part on quicksort
  • Linear search
  • Binary search
  • Graph Representation
  • Graph representation Adjacency List, Matrix & Cost
Intermediate Module
  • Minimum spanning tree algorithms
  • Prims minimum spanning tree algorithm (Greedy Algorithm)
  • Kruskal's minimum spanning tree algorithm (Greedy Algorithm)
  • Shortest path algorithms (Dijkstra /Single source)
  • Shortest path algorithms (Bellman-Ford)
  • Draw Backs of Bellman-Ford & Coding Example with python
  • Introduction to Trees
  • Binary Trees & Types
  • Representations of Tree
  • Traversal in Binary Tree In order, Post order & Pre Order Traversal
  • Coding Part on Traversal in BInary Tree
  • Binary Search Tree
  • Operation in Binary Search Tree (Search, Insertion & Deletion)
  • Coding Example for BST Operation
  • AVL Trees Introduction Balance Factor
  • Operations On AVL Trees Insertion & Rotations RL,RR ,LR rotations
  • Coding Example on AVl Tree
Advanced Module
  • Hashing basics, Functions and tables
  • Collision resolution techniques
  • Coding example on Hashing
  • Dynamic programming basics
  • Knapsack problem 0/1
  • Knapsack problem with subset
  • Coding Example Knapsack problem
  • Multi Stage Graph Problem
  • Coding Example on Multi Stage Graph Problem
  • Travelling Sales Person Shortest path - 1
  • Travelling Sales Person Shortest path-2
  • Huffman coding
  • Job Sequence Problem using Greedy method
  • Heaps introduction & Min-Max
  • Heap
  • Heap Operation Insertion & Deletion
  • Heap Creation & Heap Sort
Export Module
  • Heapify & Priority Queues
  • Coding Example on Heap Opearations
  • Tower of Hanoi
  • BFS & DFS Data Structures
  • N-Queens Problem
Building a Chatbot Using Python
Beginner Module
  • Introduction
  • Setting Up Environment
Intermediate Module
  • Data Preprocessing
Advanced Module
  • Chatbot Logic
  • Rule-Based Chatbot
Expert Module
  • Simple AI-Chatbotl

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AI Fundamentals
Beginner Module
  • Course Introduction
  • Descriptive Statistics
  • Levels of Data
  • Measures of Variability
  • Quartiles
  • Outlier
  • Probabilities
Intermediate Module
  • Introduction to AI
  • Domains of AI
  • AI Life cycle
Advanced Module
  • Introduction to ML
  • Supervised learning
  • Unsupervised learning
  • Evaluation of algorithms
Expert Module
  • Introduction to NLP
  • Steps of NLP
  • TF-Idf
  • Introduction to Computer Vision
  • Introduction to ANN
  • Introduction to CNN
  • CNN Operations
Introduction to Machine Learning
Beginner Module
  • Introduction to Machine Learning
  • Types of ML- Supervised Learning
  • Types of ML- Unsupervised Learning
  • Types of ML - Reinforcement Learning
Intermediate Module
  • Data Handling in ML - Introduction
  • Data Handling in ML - Data Cleaning
  • Supervised Learning - Linear Regression
  • Supervised Learning - Decision Tree & Ensemble Methods
Advanced Module
  • Unsupervised Learning - Clustering
  • Unsupervised Learning - Association
  • Unsupervised Learning - PCA
  • Model Evaluation
Export Module
  • Case Study- 1 E-Commerce Recommendation
  • Case Study- 2 Heart Disease Prediction
  • Case Study- 3 Finance Fraud Detection
  • Introduction to Generative AI
  • Advanced Topics - Transfer Learning
  • Advanced Topics - AutoML
Scikit-Learn for Machine Learning Part-1
Beginner Module
  • Introduction to scikit-learn for machine learning
  • Preprocessing & Pipelines (Advanced): Preprocessing Data
  • Preprocessing & Pipelines (Advanced): Preprocessing Data - Practical 1
  • Preprocessing & Pipelines (Advanced): Preprocessing Data - Practical 2
Intermediate Module
  • Preprocessing & Pipelines (Advanced): Handling Missing Data
  • Preprocessing & Pipelines (Advanced): Handling Missing Data -
  • Practical
  • Classification: Supervised learning workflow
  • Classification: Binary Classification(Log Reg)
Advanced Module
  • Classification: k-Nearest Neighbors - 1
  • Practical(Classification: k-Nearest Neighbors) - 2
  • Classification : DT and Ensemble Learnings
  • Classification : DT and Ensemble Learnings - Practical
Expert Module
  • Hyperparameter Tuning
  • Hyperparameter Tuning - Practical
  • Conclusion
Python SciPy
Beginner Module
  • Introduction
  • Jupyter Notebook & Google Colab setup, installing SciPy
  • Installing Anaconda
  • Install Scipy Numpy Pandas matplotlib and Overview
  • 1.2.4 Google Colab
  • SciPy Architecture & Ecosystem
  • 1.4.1 Scipy.Constants
  • Scipy.constants in SciPy - Practical
  • Scipy.special in Scipy
  • Scipy.special in Scipy Practical.
Intermediate Module
  • scipy.integrate
  • Scipy.integrate practical
  • Scipy.optimize
  • Scipy.optimize practical
  • Scipy.linalg.
  • Scipy.linalg practical
  • Scipy.fft
  • Scipy.fft practical
  • Scipy.signal
  • Scipy.signal practical 
Advanced Module
  • Scipy.interpolate
  • Scipy.interpolate practical
  • Scipy.spatial
  • Scipy.spatial practical
  • Scipy.sparse
  • Scipy.sparse practical
  • Scipy.stats
  • Scipy.stats practical
  • Scipy.ndimage
  • Scipy.ndimage Practical
Export Module
  • EDA with SciPy & Pandas
  • EDA with SciPy & Pandas Practical
  • Use Cases & Projects: Signal denoising
  • Use Cases & Projects: Signal denoising Practical
  • Use Cases & Projects: Image Filtering
  • Use Cases & Projects: Image Filtering Practical
  • Use Cases & Projects: statistical modeling
  • Use Cases & Projects: statistical modeling practical
  • Wrap-up & Resources
NLTK
Beginner Module
  • Introduction to NLP & NLTK Ecosystem
  • Text Preprocessing Basics - Part 1
  • Text Preprocessing Basics - Part 2
Intermediate Module
  • POS Tagging & Named Entity Recognition - Part 1
  • POS Tagging & Named Entity Recognition - Part 2
  • Text Statistics & Visualization - Part 1
  • Text Statistics & Visualization - Part 2
  • Corpora & WordNet - Part 1
  • Corpora & WordNet - Part 2
Advanced Module
  • Text Classification with Naive Bayes - Part 1
  • Text Classification with Naive Bayes - Part 2
  • Chunking & Grammar Parsing
Export Module
  • Sentiment Analysis Project - Part 1
  • Sentiment Analysis Project - Part 2
  • ML Integration & Course Wrap-Up
Computer vision project
Beginner Module
  • Introduction
  • Basic Computer vision theory (edited)
  • Basics of CNN
Intermediate Module
  • Basic Image Operations
  • Basic Image Operations with OpenCV (edited Ver 2.0)
  • Video capture with OpenCV
  • OpenCV_deep_learning_intro
  • OpenCV_face_detection_demo
Advanced Module
  • Hand Detection and Tracking Introduction (edited)
  • Basic Hand Tracking Basic Script
  • Hand Tracking Module
  • Hand Gesture controlled volume control
Export Module
  • Eye tracking introduction
  • Eye tracking Coding
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