Admissions Open for JANUARY Batch
Learn the foundations of AI, including machine learning, NLP, and computer vision, to build skills and plan a career in artificial intelligence
Days : Sat & Sun
Duration : 20 Hours
Timings: 8 - 10 PM IST
Try Risk-free, 15 Days Money Back Guarantee
20 Hours
9 - 10 PM IST
Tue & Thu
Python Level - 1
Learn Python for AI through data handling, visualization, LLM projects, and AI-driven app development using neural networks and pretrained models.
Online Live Instructor-Led Learning
20 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Python Foundations for AI with Libraries and Tools
MiniProjects like MealPlanner, Birthday Reminders
Data Handling & Visualizations of your Data with Graphs, Charts, EDA, Aggregation
Vibe Coding with LLMs
Create Your AI Web App with tools like streamlit/Tkinter
Get familier with
AI/ML Cycle - Full stack experience
Working on Pretrained AI Models
Understand the Neural Networks, and ethics behind models
Build a micro SaaS AI-powered web application (Streamlit/Tkinter)
Object Detections/Classifications
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners who have completed Python Foundation and want to deepen their Python skills
Prerequisites
Strong pythong foundation
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Strong pythong foundation
Who Should Enroll?
This course is for learners who have completed Python Foundation and want to deepen their Python skills
By end of this course
Get Stronger in
- Python Foundations for AI with Libraries and Tools
- MiniProjects like MealPlanner, Birthday Reminders
- Data Handling & Visualizations of your Data with Graphs, Charts, EDA, Aggregation
Get Familiar in
- AI/ML Cycle – Full stack experience
- Working on Pretrained AI Models
- Understand the Neural Networks, and ethics behind models
Course Contents
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities
Phase 2: Data Fluency & Analytical Thinking
Dive into CSV, JSON, and image datasets from familiar domains, learning to connect data to real-life systems and building intuition about how data flows through applications.
Master essential data preparation techniques using .dropna(), .groupby(), .fillna(), and string functions to transform messy data into clean, analysis-ready datasets that lay foundations for AI applications.
Create compelling bar, line, and scatter plots using matplotlib and seaborn to tell meaningful stories with data, learning to communicate insights effectively through visual narratives
Students choose their own dataset, clean → visualize → explain insights, experiencing their first analytical data storytelling project and building confidence in data-driven communication.
Phase 2: Data Fluency & Analytical Thinking
Dive into CSV, JSON, and image datasets from familiar domains, learning to connect data to real-life systems and building intuition about how data flows through applications.
Master essential data preparation techniques using .dropna(), .groupby(), .fillna(), and string functions to transform messy data into clean, analysis-ready datasets that lay foundations for AI applications.
Create compelling bar, line, and scatter plots using matplotlib and seaborn to tell meaningful stories with data, learning to communicate insights effectively through visual narratives
Students choose their own dataset, clean → visualize → explain insights, experiencing their first analytical data storytelling project and building confidence in data-driven communication.
Phase 3: From Code to Intelligence
Construct a complete script to load → clean → analyze → visualize a dataset, learning to think in workflows rather than individual tasks and understanding AI-ready data architecture
Create a spam classifier using rules, discover its limitations, then make the key conceptual shift to “learning from data” rather than programmed instructions, understanding how AI learns instead of just obeying
Take a visual walkthrough of layers, neurons, and weights using analogies and simple math demonstrations to understand model anatomy and how AI makes decisions under the hood.
Get hands-on neural network experience by constructing a 2-layer NN for XOR or simple number classification, learning to build → test → tune your own AI models
Train image/video classifiers for object detection and classification, understanding real-world AI systems architecture and how professional AI models work in practice
Phase 3: From Code to Intelligence
Construct a complete script to load → clean → analyze → visualize a dataset, learning to think in workflows rather than individual tasks and understanding AI-ready data architecture
Create a spam classifier using rules, discover its limitations, then make the key conceptual shift to “learning from data” rather than programmed instructions, understanding how AI learns instead of just obeying
Take a visual walkthrough of layers, neurons, and weights using analogies and simple math demonstrations to understand model anatomy and how AI makes decisions under the hood.
Get hands-on neural network experience by constructing a 2-layer NN for XOR or simple number classification, learning to build → test → tune your own AI models
Train image/video classifiers for object detection and classification, understanding real-world AI systems architecture and how professional AI models work in practice
Phase 4: Build AI Applications
Build an AI-powered interface using Streamlit with the complete input → model → prediction flow, creating your first full-stack AI experience with working UI.
Master the complete visual walkthrough of input → model → inference → feedback cycle, understanding how all components of AI applications work together seamlessly.
Explore fairness in facial recognition, biased datasets, and responsible design principles, building ethical awareness and social responsibility as an AI developer.
Phase 4: Build AI Applications
Build an AI-powered interface using Streamlit with the complete input → model → prediction flow, creating your first full-stack AI experience with working UI.
Master the complete visual walkthrough of input → model → inference → feedback cycle, understanding how all components of AI applications work together seamlessly.
Explore fairness in facial recognition, biased datasets, and responsible design principles, building ethical awareness and social responsibility as an AI developer.
Phase 5: Thoughtful Builders & Responsible
Design and prototype an AI idea that solves a real-world problem ethically, practicing responsible innovation and learning project ideation plus scoping skills
Use AI to teach AI by simplifying a complex ML concept for a 10-year-old using analogies, achieving deeper mastery through teaching and improving communication skills.
Phase 5: Thoughtful Builders & Responsible
Design and prototype an AI idea that solves a real-world problem ethically, practicing responsible innovation and learning project ideation plus scoping skills
Use AI to teach AI by simplifying a complex ML concept for a 10-year-old using analogies, achieving deeper mastery through teaching and improving communication skills.
Phase 6: Capstone Challenge & Showcase
Choose your domain (e.g., emotion detection, recipe AI), define the problem, sketch your workflow, and select your dataset for a comprehensive AI application project.
Build and demo your Streamlit AI app with clear UI, working model, and thoughtful explanation, showcasing your full-stack AI experience with real-world presentation skills.
Phase 6: Capstone Challenge & Showcase
Choose your domain (e.g., emotion detection, recipe AI), define the problem, sketch your workflow, and select your dataset for a comprehensive AI application project.
Build and demo your Streamlit AI app with clear UI, working model, and thoughtful explanation, showcasing your full-stack AI experience with real-world presentation skills.