AI ka chilla started now - Codanics

Course Content

00: Resources

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Links to All codes

01: Introduction to the course

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AI ka chilla started now

AI KA Chilla-2026 | Learn AI in 40 Days in 2026 #NewYear #newyear2026

Day-1: Introduction to the course and plans

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Start of the course and Happy New Year

Enroll in AI ka chilla LMS check your email

Discord community link for AI ka chilla

Meeting details for live classes and question answer sessions

Know your instructor

Plan of the course AI ka chilla learning map

Daily tasks for students Important

Questions No. 1: Non structured and structured data analysis

Question No. 2: Projects for different sectors for using AI

Question no. 3: How to pitch ourselves on linkedIn after the course?

Day-2: Python and other Software Installations + Integration

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Introduction to Python as programming language

Guido von Rossum creator of Python as programming language

Five software installation for the course

Create new user account for fresh installations and admin privileges

Install python with a fresh start from Zero

Install VScode with fresh start

VScode options while installations goo for future coding

Install git and gitBash in windows

Install miniconda for AI and python environments

gitBash – miniconda – vscode integration | step by step guide

miniconda and gitbash integration trouble shooting – Important

Day-3: Essentials (GitHub, GitHub student/teacher developer pack, Python and learning)

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GitHub account and GitHub student developer pack

GitHub account in VScode for synchronization

Tools to plan your tasks for longer run and learning methodology

Your first line of code | use python with three ways

Learn python crash course before we proceed

Three (3) way learning strategy | Tips to Master coding and learning by doing

We have covered so far…..

Trouble shooting and errors you might be having

Important login IDs and signups for AI journey

News and Newsletters for AI updates on regular basis

Mobile price vs. Your Laptop price Data analysis

Practice to use discord server private channel for the course

Question No. 1: What is the plan for learning ahead

Question No. 2: How can we post on LinkedIN from News?

Question No. 3: Data analysis plan from now on

Question No. 4: Industry vs. Academia for AI

Question No. 5: Integration of AI in industrial Tools

Day-4: Python Programming A-Z

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Learn python crash course before we proceed (This was also in the pre-requisite)

Jupyter notebooks (Introduction) | comments and use in python

Install Jupyter lab and Jupyter notebooks windows

Jupyter notebooks use in google colab and kaggle

Jupyter notebook in VScode

Why VScode is my favourite IDE (Integrated Development Environment)

Environments and conflicts | conda, mamba, venv (virtual) environments for installing softwares

GitHub copilot and Snake game development under 5 minutes

Networking is important for AI learning

Portfolio is important for AI Engineering journey these days

Free vs. Paid tools for portfolio buildup

Tools to build portfolio for AI engineering Journey

Question No. 1 from Students

Question No. 2 from Students

Question No. 3 from Students

Question No. 4 from Students

Question No. 5 from Students

Question No. 6 from Students

MarkDown in 72 minutes crash course

Day-5: Networking and Portfolio for AI or Data Science Students

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Networking using LinkedIN | Reading Material

Portfolio making | Understanding and building portfolio for Data Science and AI Students and Developers

Day-6: Git and GitHub | A-Z Guide using only command line | Beginner to Expert

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Git learning pathway in Two Steps

Git Bash installation (ignore if you already installed)

Verify your installation (Important)

Configure your local Git with username and email from gitHub

Create a local repository or folder

Initialize git to turn on track changes

git commands | git add and commit to stage and store changes

git log or history of commits

Restore a file to a specific commit

Push local repository to github account | Important

Day-7: Git, GitHub and VScode use as professionals

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VScode to initialize git repository using GUI

Local to online git repository on VScode and GitHub

Limit for file sizes on GitHub

Connect your current repo to GitHub with VScode

Work with online repositories of GitHub

Add collaborators in GitHub repositories | Work in a team

Day-8: Large Language Models (LLMs)

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01-What are Large Language Models and how do they work?

02-Trending List of LLMs and their use cases

03-Differences in LLMs vs. Fine Tuned LLMs vs. RAG based LLMs

04-Understanding RAG based Bots (Retrieval Augmented Generation)

05-Augmented Generation to improve LLM RAG and CAG

06-Earn USDs using RAG and CAG

07-Ten Lessons from an expert: Enterprise level RAG based apps

Day-9: Generative AI (GenAI) and Agentic AI

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01-What is Generative AI? Easy way to learn

02-GenAI applications for project development

03-GenAI for online earning by making projects and providing services

04-GenAI APIs to make tools and applications for earning

05-GenAI vs AgenticAI what is the difference?

06-Hundred Plus GenAI projects to earn online

Day-10: VScode + GitHub copilot and VIBE coding | Data and Data Components

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VScode IDE and Alternatives to GitHub Copilot for VScode

VIBE coding with IDEs and coding agents for fast programming

Product requirements document | PRD is important

Workflow for Product development and Finding a Niche

Data and Types of Data | Scales or levels of measurement

Question:1-RAG based Apps and future career

Define data in an easy way

Categorical vs. Numerical data | Qualitative vs. Quantitative Data

Text Data of Natural Language | NLP | Natural Language Processing

Data in spreadsheets and Important components of data

Day-11: Data, Data Gathering and Project based Product Development with Trending Topic

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Variables and Metadata about variables

Which data should we work on and why?

First hand information gathering about AI and Data Science

Google sheet for Data Gathering websites

Save this sheet in your pc for future

Valid and Authentic Resources to get data

Task for students | Use Pandas Profiling for example dataset

Important Notes for the Data Measurements and Scales

Day-12: Data and Pandas profiling | Exploratory Data Analysis (EDA)

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Plan to use pandas- or ydata- profiling for Automatic EDA

EDA project with pandas- or ydata- profiling for three different datasets

Automatic EDA in few seconds using data from kaggle.com

Day-13: Data Gathering and automatic EDA with Top Libraries

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Data Gathering Using Python APIs | wbdata, FAOSTAT, Eurostat, yfinance for Analysts & Scientists

Complete EDA with dTale library in Python in seconds with only 5 lines of code

Pandas- or ydata-profiling for time series analysis and automatic EDA

dtale library for automatic EDA for any data

Data Collection is important for EDA

Descriptive Statistics is important to learn for EDA

Inferential Statistics and Hypothesis Testing for comparison

Day-14: Do EDA in few seconds with these Python Libraries

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PyGwalker for Automatic Data Visualization and EDA

Sweetviz use in python comes with errors

Sweetviz is working fine here for automatic EDA

SKIMPY python library for data understanding

Dataprep python library for EDA of Data

LIDA python library for EDA and auto data analysis

Lida Huggingface take s alot of space

All codes for these libraries are here

Must Read this guide about EDA

EDA with LIDA and A book chapter about EDA to understand outputs of Lida

LIDA is working using API keys | I just Bought

Always close the server when you are running server based apps

Day-15: AI for Vibe Coding

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Pandas AI library for AI data analysis

Basic Data Knowledge is important before you use AI for Data Analysis

AI based IDE Cursor for APP development in Seconds

Antigravity is a great alternative to VScode to develop application using agentic AI in seconds

Day-16: Antigravity and Project with Agentic AI for Kaggle Data Grandmaster Ranking

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Google Antigravity is RAM Hungry

Google Antigravity IDE can work as a developer in 21000 PKR only for an year

Google One AI Enhanced plans for Antigravity

Kaggle Dataset Grandmaster guide A to Z in urdu using Google Antigravity AI Agents

Important Task Assignment for Students

Day-17: A complete project using Agentic AI

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Gathering data to Uploading on Kaggle with Analysis A Complete Project for AI practitioners

Why do we need to learn Basics of EDA and data for trends?

Google Antigravity IDE is best but?

Claude Code is new AI agentic Tool to make products

Tasks and future works

Day-18: Claude Code and LLM based Application development planing

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Claude code and use of API in LLM based applications

Think about 5 ideas for LLM based applications

Write your ideas in this Google sheet

Three major niches to work for projects in Pakistan

Google ka Nano Banana for Image Generation

Day-19: Data Pre-processing, KEY to AI

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Master Data Preprocessing, Wrangling, and Cleaning for Machine Learning Projects!

Day-20: Web-scraping for Data Collection in Python

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A hands on Training and ideas for Web-Scraping in Python

Day-21: Data Visualization in Python | Konsa Plot kahan bnana hy?

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Data Visualization Masterclass in Python | Matplotlib, Seaborn & Plotly for Beginners to Advanced

Day-22: Power BI for Data Visualization and DashBoards | No coding tool

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Power BI Complete Course

Day-23: Tableau for Data Visualization and DashBoar development | No-code

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Tableau for Beginners | Complete Course

Day-24: LLMs – Local vs. API based

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Summary of Previous Lectures

Google Antigravity Issues with Limits and cool down periods

Kaggle Ranking for your CV is important to grab jobs and clients

API keys of Proprietary Models and Local Open source Models for AI

API keys or Local Model? Which one should we use and why?

Open-source vs. closed source Large Language Models

Ollama for Locally deployed models in our Apps

Which model should you select and why in Ollama?

Day-25: LLM integrated application | A complete project

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LLM integration in application | Workflow (1/5)

Building LLM based ChatBot with APIs from OpenAI (2/5)

Task to deploy application on streamlit application (3/5)

Task with additional features (4/5)

Application deployment for free on streamlit cloud (5/5)

Day-26: Feature Enriched ChatBot development and deployment

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Feature Enriched ChatBot development to chat with pdf, docx, txt, tsv, csv and txt data files

Day-27: LLM Integration with Local Models using Ollama

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Ollama to use LLMs locally inside your PC

Ollama LLM integration with Local Models without API cost

Always kill terminal app backend process

Day-28: Machine Learning and Students’ projects

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LLM Apps built by students

Machine Learning Basics in Python

Turn off auto dub mode in Youtube for better understanding

Youtube Auto dubbing function is making videos in AI audio | AI English and Urdu

Question about URDU GPT

Day-29: Machine Learning Complete Course

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Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-1/2)

Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-2/2)

Day-30: Automatic Machine Learning few lines of code and no code tool

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PyCaret for Automatic ML | PyCaret Installation

Search PyCaret usage and quick start

Regression problem with PyCaret

Classification Problem with PyCaret

Project Application | AutoMated Machine Learning Application Development using PyCaret

Day-31: Deep Learning Compete Crash Course

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Deep Learning Crash Course Part-1 | Master Neural Networks & AI Fundamentals

Deep Learning Crash Course Part-2 | Master Neural Networks & AI Fundamentals

Tensorflow and Pytorch for Deep Learning

Deep Learning using Agentic AI for Silver Price Prediction

Day-32: Docker Container

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Docker | Deploy Any Python App with Docker Containers – Step-by-Step Guide (2025)

Day-33: Complete Data Analytics Project

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Complete Data Analytics Project with Report A-Z | Python, VS Code & GitHub | Tips Dataset

Day-34: Prompt Engineering and Descriptive Statistics

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Descriptive Statistics for Data Analytics – Prompt Engineering with ChatGPT A-Z guide

Day-35: Content and Scientific Writing with AI

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Scientific Writing Master Class (For Interested Students Only)

Using AI tools in Scientific Writing Complete Course

Day-36: Graph Theory for n8n

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Graph Theory Meets n8n: Build No-Code AI Agent Workflows A-Z guide

Day-37: AI Agents with Real World Applications

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We have learned so far….

AI Agents and their use cases in Real World

AI Agent working with Discord server Bot and LLM to refine texts (Part-1/2)

AI Agent working with Discord server Bot and LLM to refine texts (Part- 2/2)

Day-38: Hostinger for AI Agents and Application deployment

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Hostinger KVM VPS is the best for application deployment

End to End python application building and deployment to Hostinger KVM VPS server

AI Ka Chilla 2026

Information about AI Ka Chilla 2026 complete course content and registration is given here: https://codanics.com/aikachilla/

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