AI Ka Chilla 2026 Fine-Tune a LLM in VS Code & Deploy to Hugging Face - Full Hands-On Python Tutorial
AI Ka Chilla 2026
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00: Resources
Links to All codes
Reading
01: Introduction to the course
AI ka chilla started now
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AI KA Chilla-2026 | Learn AI in 40 Days in 2026 #NewYear #newyear2026
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Day-1: Introduction to the course and plans
Start of the course and Happy New Year
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Enroll in AI ka chilla LMS check your email
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Discord community link for AI ka chilla
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Meeting details for live classes and question answer sessions
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Know your instructor
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Plan of the course AI ka chilla learning map
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Daily tasks for students Important
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Questions No. 1: Non structured and structured data analysis
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Question No. 2: Projects for different sectors for using AI
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Question no. 3: How to pitch ourselves on linkedIn after the course?
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Day-2: Python and other Software Installations + Integration
Introduction to Python as programming language
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Guido von Rossum creator of Python as programming language
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Five software installation for the course
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Create new user account for fresh installations and admin privileges
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Install python with a fresh start from Zero
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Install VScode with fresh start
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VScode options while installations goo for future coding
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Install git and gitBash in windows
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Install miniconda for AI and python environments
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gitBash - miniconda - vscode integration | step by step guide
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miniconda and gitbash integration trouble shooting - Important
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Day-3: Essentials (GitHub, GitHub student/teacher developer pack, Python and learning)
GitHub account and GitHub student developer pack
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GitHub account in VScode for synchronization
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Tools to plan your tasks for longer run and learning methodology
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Your first line of code | use python with three ways
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Learn python crash course before we proceed
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Three (3) way learning strategy | Tips to Master coding and learning by doing
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We have covered so far.....
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Trouble shooting and errors you might be having
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Important login IDs and signups for AI journey
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News and Newsletters for AI updates on regular basis
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Mobile price vs. Your Laptop price Data analysis
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Practice to use discord server private channel for the course
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Question No. 1: What is the plan for learning ahead
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Question No. 2: How can we post on LinkedIN from News?
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Question No. 3: Data analysis plan from now on
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Question No. 4: Industry vs. Academia for AI
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Question No. 5: Integration of AI in industrial Tools
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Day-4: Python Programming A-Z
Learn python crash course before we proceed (This was also in the pre-requisite)
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Jupyter notebooks (Introduction) | comments and use in python
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Install Jupyter lab and Jupyter notebooks windows
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Jupyter notebooks use in google colab and kaggle
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Jupyter notebook in VScode
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Why VScode is my favourite IDE (Integrated Development Environment)
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Environments and conflicts | conda, mamba, venv (virtual) environments for installing softwares
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GitHub copilot and Snake game development under 5 minutes
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Networking is important for AI learning
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Portfolio is important for AI Engineering journey these days
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Free vs. Paid tools for portfolio buildup
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Tools to build portfolio for AI engineering Journey
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Question No. 1 from Students
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Question No. 2 from Students
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Question No. 3 from Students
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Question No. 4 from Students
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Question No. 5 from Students
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Question No. 6 from Students
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MarkDown in 72 minutes crash course
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Day-5: Networking and Portfolio for AI or Data Science Students
Networking using LinkedIN | Reading Material
Reading
Portfolio making | Understanding and building portfolio for Data Science and AI Students and Developers
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Day-6: Git and GitHub | A-Z Guide using only command line | Beginner to Expert
Intro to Git
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How git works?
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Is git free?
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Git learning pathway in Two Steps
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Install git
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Git Bash installation (ignore if you already installed)
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Verify your installation (Important)
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Configure your local Git with username and email from gitHub
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Create a local repository or folder
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Initialize git to turn on track changes
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git commands | git add and commit to stage and store changes
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git log or history of commits
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Restore a file to a specific commit
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Push local repository to github account | Important
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Day-7: Git, GitHub and VScode use as professionals
VScode to initialize git repository using GUI
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Local to online git repository on VScode and GitHub
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Limit for file sizes on GitHub
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Connect your current repo to GitHub with VScode
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Work with online repositories of GitHub
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Add collaborators in GitHub repositories | Work in a team
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Day-8: Large Language Models (LLMs)
01-What are Large Language Models and how do they work?
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02-Trending List of LLMs and their use cases
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03-Differences in LLMs vs. Fine Tuned LLMs vs. RAG based LLMs
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04-Understanding RAG based Bots (Retrieval Augmented Generation)
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05-Augmented Generation to improve LLM RAG and CAG
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06-Earn USDs using RAG and CAG
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07-Ten Lessons from an expert: Enterprise level RAG based apps
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Day-9: Generative AI (GenAI) and Agentic AI
01-What is Generative AI? Easy way to learn
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02-GenAI applications for project development
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03-GenAI for online earning by making projects and providing services
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04-GenAI APIs to make tools and applications for earning
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05-GenAI vs AgenticAI what is the difference?
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06-Hundred Plus GenAI projects to earn online
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Day-10: VScode + GitHub copilot and VIBE coding | Data and Data Components
VScode IDE and Alternatives to GitHub Copilot for VScode
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VIBE coding with IDEs and coding agents for fast programming
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Product requirements document | PRD is important
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Workflow for Product development and Finding a Niche
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Data and Types of Data | Scales or levels of measurement
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Question:1-RAG based Apps and future career
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Define data in an easy way
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Categorical vs. Numerical data | Qualitative vs. Quantitative Data
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Text Data of Natural Language | NLP | Natural Language Processing
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Data in spreadsheets and Important components of data
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Day-11: Data, Data Gathering and Project based Product Development with Trending Topic
Variables and Metadata about variables
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Which data should we work on and why?
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First hand information gathering about AI and Data Science
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Google sheet for Data Gathering websites
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Save this sheet in your pc for future
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Valid and Authentic Resources to get data
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Task for students | Use Pandas Profiling for example dataset
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Important Notes for the Data Measurements and Scales
Reading
Day-12: Data and Pandas profiling | Exploratory Data Analysis (EDA)
Task for Today
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Plan to use pandas- or ydata- profiling for Automatic EDA
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EDA project with pandas- or ydata- profiling for three different datasets
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Automatic EDA in few seconds using data from kaggle.com
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Day-13: Data Gathering and automatic EDA with Top Libraries
Data Gathering Using Python APIs | wbdata, FAOSTAT, Eurostat, yfinance for Analysts & Scientists
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Complete EDA with dTale library in Python in seconds with only 5 lines of code
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Pandas- or ydata-profiling for time series analysis and automatic EDA
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dtale library for automatic EDA for any data
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Data Collection is important for EDA
Reading
Descriptive Statistics is important to learn for EDA
Reading
Inferential Statistics and Hypothesis Testing for comparison
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Day-14: Do EDA in few seconds with these Python Libraries
PyGwalker for Automatic Data Visualization and EDA
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Sweetviz use in python comes with errors
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Sweetviz is working fine here for automatic EDA
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SKIMPY python library for data understanding
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Dataprep python library for EDA of Data
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LIDA python library for EDA and auto data analysis
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Lida Huggingface take s alot of space
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All codes for these libraries are here
Reading
Must Read this guide about EDA
Reading
EDA with LIDA and A book chapter about EDA to understand outputs of Lida
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LIDA is working using API keys | I just Bought
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Always close the server when you are running server based apps
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Day-15: AI for Vibe Coding
Pandas AI library for AI data analysis
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Basic Data Knowledge is important before you use AI for Data Analysis
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AI based IDE Cursor for APP development in Seconds
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Antigravity is a great alternative to VScode to develop application using agentic AI in seconds
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Day-16: Antigravity and Project with Agentic AI for Kaggle Data Grandmaster Ranking
Google Antigravity is RAM Hungry
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Google Antigravity IDE can work as a developer in 21000 PKR only for an year
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Google One AI Enhanced plans for Antigravity
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Kaggle Dataset Grandmaster guide A to Z in urdu using Google Antigravity AI Agents
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Important Task Assignment for Students
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Day-17: A complete project using Agentic AI
Gathering data to Uploading on Kaggle with Analysis A Complete Project for AI practitioners
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Why do we need to learn Basics of EDA and data for trends?
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Google Antigravity IDE is best but?
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Claude Code is new AI agentic Tool to make products
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Tasks and future works
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Day-18: Claude Code and LLM based Application development planing
Claude code and use of API in LLM based applications
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Think about 5 ideas for LLM based applications
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Write your ideas in this Google sheet
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Three major niches to work for projects in Pakistan
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Google ka Nano Banana for Image Generation
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Day-19: Data Pre-processing, KEY to AI
Master Data Preprocessing, Wrangling, and Cleaning for Machine Learning Projects!
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Day-20: Web-scraping for Data Collection in Python
A hands on Training and ideas for Web-Scraping in Python
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Day-21: Data Visualization in Python | Konsa Plot kahan bnana hy?
Data Visualization Masterclass in Python | Matplotlib, Seaborn & Plotly for Beginners to Advanced
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Day-22: Power BI for Data Visualization and DashBoards | No coding tool
Power BI Complete Course
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Day-23: Tableau for Data Visualization and DashBoar development | No-code
Tableau for Beginners | Complete Course
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Day-24: LLMs – Local vs. API based
Summary of Previous Lectures
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Google Antigravity Issues with Limits and cool down periods
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Kaggle Ranking for your CV is important to grab jobs and clients
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API keys of Proprietary Models and Local Open source Models for AI
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API keys or Local Model? Which one should we use and why?
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Open-source vs. closed source Large Language Models
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Ollama for Locally deployed models in our Apps
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Which model should you select and why in Ollama?
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Day-25: LLM integrated application | A complete project
LLM integration in application | Workflow (1/5)
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Building LLM based ChatBot with APIs from OpenAI (2/5)
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Task to deploy application on streamlit application (3/5)
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Task with additional features (4/5)
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Application deployment for free on streamlit cloud (5/5)
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Day-26: Feature Enriched ChatBot development and deployment
Feature Enriched ChatBot development to chat with pdf, docx, txt, tsv, csv and txt data files
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Day-27: LLM Integration with Local Models using Ollama
Ollama to use LLMs locally inside your PC
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Ollama LLM integration with Local Models without API cost
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Always kill terminal app backend process
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Day-28: Machine Learning and Students’ projects
LLM Apps built by students
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Machine Learning Basics in Python
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Turn off auto dub mode in Youtube for better understanding
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Youtube Auto dubbing function is making videos in AI audio | AI English and Urdu
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Question about URDU GPT
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Day-29: Machine Learning Complete Course
Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-1/2)
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Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-2/2)
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Day-30: Automatic Machine Learning few lines of code and no code tool
PyCaret for Automatic ML | PyCaret Installation
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Search PyCaret usage and quick start
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Regression problem with PyCaret
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Classification Problem with PyCaret
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Project Application | AutoMated Machine Learning Application Development using PyCaret
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Day-31: Deep Learning Compete Crash Course
Deep Learning Crash Course Part-1 | Master Neural Networks & AI Fundamentals
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Deep Learning Crash Course Part-2 | Master Neural Networks & AI Fundamentals
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Tensorflow and Pytorch for Deep Learning
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Deep Learning using Agentic AI for Silver Price Prediction
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Day-32: Docker Container
Docker | Deploy Any Python App with Docker Containers - Step-by-Step Guide (2025)
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Day-33: Complete Data Analytics Project
Complete Data Analytics Project with Report A-Z | Python, VS Code & GitHub | Tips Dataset
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Day-34: Prompt Engineering and Descriptive Statistics
Descriptive Statistics for Data Analytics – Prompt Engineering with ChatGPT A-Z guide
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Day-35: Content and Scientific Writing with AI
Scientific Writing Master Class (For Interested Students Only)
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Using AI tools in Scientific Writing Complete Course
Reading
Day-36: Graph Theory for n8n
Graph Theory Meets n8n: Build No-Code AI Agent Workflows A-Z guide
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Day-37: AI Agents with Real World Applications
We have learned so far....
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AI Agents and their use cases in Real World
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AI Agent working with Discord server Bot and LLM to refine texts (Part-1/2)
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AI Agent working with Discord server Bot and LLM to refine texts (Part- 2/2)
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Day-38: Hostinger for AI Agents and Application deployment
Hostinger KVM VPS is the best for application deployment
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End to End python application building and deployment to Hostinger KVM VPS server
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Day-39: Fine tuning LLM
Differences in LLMs vs. Fine Tuned LLMs vs. RAG based LLMs
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Why Fine-Tune & When to Use RAG – Complete Guide
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Fine-Tune a LLM in VS Code & Deploy to Hugging Face - Full Hands-On Python Tutorial
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Day-40: More Data Science Dimensions
Time Series Data Analysis & Forecasting using ML/DL | Crash Course | Urdu/Hindi | Under 11 Hours
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Structured Query Language (SQL) for Data Analytics and Data Science | 2 Hours
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Reminder - Portfolio: Build & Deploy Your Data Science & AI Portfolio – Complete Crash Course
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Docker | Deploy Any Python App with Docker Containers - Step-by-Step Guide (2025)
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Certificate will only be provided upon submission of a project + all assignments from this course
Reading
More Crash Courses will be coming soon in this section......
Reading
Announcements Reviews Live Classes Gradebook Course Info
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