Programmer’s Picnic | Learn With Champak

AI-ML with Python
16-Week Complete Roadmap

A beginner-friendly, project-based AI-ML learning path where you join the course, complete structured lessons, get guidance on WhatsApp and Zoom, ask up to 10 queries every week, and finish with a portfolio-ready capstone.

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16Weeks
ZoomLive guidance
WhatsAppDoubt support
10/weekStudent queries

Complete Class Schedule

Alternate day live classes starting from Monday, with assignments, doubts, weekly projects, WhatsApp guidance, Zoom support, and up to 10 course-related queries per week.

Monday

Live Class
Evening 7:00 PM – 8:30 PM
Live on Zoom

Tuesday

Assignments + Doubts
Practice discussion on WhatsApp group

Wednesday

Live Class
Evening 7:00 PM – 8:30 PM
Live on Zoom

Thursday

Assignments + Doubts
Revision and WhatsApp discussion

Friday

Live Class
Evening 7:00 PM – 8:30 PM
Live on Zoom

Saturday

Revision + Practice
Prepare for Sunday mini project

Sunday

Small Project
Past week discussion
Trailer of coming week

Support

Doubts and discussions continue in the WhatsApp group.

Monday: Class Tuesday: Practice Wednesday: Class Thursday: Practice Friday: Class Saturday: Revision Sunday: Project

Live Class Timing

Evening 7:00 PM to 8:30 PM. Classes are live on Zoom. Doubts and discussion will happen in the WhatsApp group.

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Guided Learning System

Learn at your own pace, but never feel alone. You complete lessons step by step, practice with clear outputs, and receive structured guidance through WhatsApp and Zoom.

Self-paced lessons with real guidance

This system is designed for students who need flexibility. You can study after school, college, work, or family duties. The course gives you a path, lesson targets, weekly practice, and human support when you get stuck.

Self-paced learning WhatsApp guidance Zoom doubt support 10 queries per week Assignments + projects
Learn Watch/read the lesson, understand the idea, and run the examples.
Complete Finish the task, notebook, code file, assignment, or mini output.
Improve Ask doubts, correct mistakes, revise, and move to the next lesson.

How the system works

Students progress through the roadmap with checkpoints instead of random videos. Every step has a purpose and a visible result.

1

Join and enter the learning circle

The student joins the course and receives the learning path, lesson order, practice expectations, and WhatsApp guidance access.

2

Learn at your own pace

The student completes lessons according to their speed. Fast students can move ahead; slower students can revise without pressure.

3

Complete lesson outputs

Each lesson should end with something visible: a working code block, Colab notebook, chart, cleaned dataset, model, or mini project.

4

Ask up to 10 queries every week

Use weekly queries for course doubts, broken code, assignment confusion, concept revision, project direction, or notebook review.

5

Get WhatsApp and Zoom guidance

Simple doubts are handled on WhatsApp. Bigger doubts, project explanations, revision, and live correction can be handled on Zoom.

What students receive

The aim is not only to “attend class.” The aim is to keep moving, complete real work, and build confidence week by week.

  • Structured AI-ML lessons from beginner level to project level
  • Self-paced completion with clear weekly direction
  • Up to 10 course-related queries per week
  • WhatsApp support for doubts, code issues, and assignments
  • Zoom guidance for deeper explanation and project discussion
  • Checkpoints after lessons: learn, code, submit, improve
  • Sunday project/revision rhythm to keep progress practical
  • Capstone support so students finish with presentable work
10/week

Every enrolled student can ask up to 10 meaningful course queries per week. This keeps support focused, fair, and useful.

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Course Journey

From foundation coding to real AI-ML notebooks and project presentation.

Foundation

Python, Colab, logic, NumPy, arrays, pandas, and CSV files.

Data Core

Cleaning, visualization, statistics, correlation, and dataset selection.

Similarity + NLP

Text preprocessing, TF-IDF, cosine similarity, and text classification.

ML Core + Capstone

Regression, classification, clustering, and a final portfolio project.

Complete 16-Week Plan

Each week has a clear learning goal, practice direction, dataset idea, and output.

Week 01Foundation

Google Colab + Python start

Introduction to Colab, cells, markdown, running code, variables, input-output, data types, operators, Python editor, and VS Code awareness.

  • Learn: notebook workflow, syntax, errors, print, comments, editor basics, and VS Code setup awareness
  • Practice: simple calculator and logic tasks inside Colab
  • Output: first personal Colab notebook and first runs in the Learn With Champak editor
Week 02Foundation

Conditions, loops, functions, and strings

Flow control, reusable logic, string handling, and problem-solving patterns.

  • Learn: if-else, for, while, functions, return, string methods
  • Practice: text cleaning and menu-based mini programs
  • Output: utility notebook with reusable functions
Week 03Foundation

Lists, dictionaries, NumPy, and arrays

Data structures plus the move from plain Python collections to numerical arrays.

  • Learn: indexing, slicing, dictionaries, NumPy arrays, shape, axis
  • Practice: compute mean, median, min, max, range with arrays
  • Output: basic numerical thinking notebook
Week 04Foundation

pandas and reading CSV files in Colab

Series, DataFrame, CSV upload, selection, filtering, and first dataset inspection.

  • Learn: read_csv, head, tail, info, describe, filtering
  • Dataset: small classroom CSV + Titanic preview, explored inside Colab and editor practice
  • Output: first data inspection report
Dataset Links Titanic CSV
Week 05Data core

Data cleaning and preparation

Missing values, duplicates, datatype fixes, string cleanup, and making data analysis-ready.

  • Learn: null handling, fillna, dropna, duplicate control, type conversion
  • Dataset: Titanic or messy student dataset
  • Output: cleaned dataset notebook with before/after comparison
Dataset Links Titanic CSV
Week 06Data core

Visualization and insight building

Bar charts, histograms, scatter plots, box plots, and how to tell a simple story from data.

  • Learn: matplotlib basics and chart interpretation
  • Dataset: Titanic, housing, or sales-style CSV
  • Output: chart-driven short analysis notebook
Dataset Links Titanic CSV Sales / Tips CSV MPG Regression CSV California Housing
Week 07Data core

Statistics for AI-ML

Mean, median, mode, variance, standard deviation, percentiles, distribution basics, and interpretation.

  • Learn: descriptive statistics from ground level
  • Practice: compute stats with NumPy and pandas
  • Output: statistics worksheet notebook
Dataset Links Tips / Stats CSV Titanic CSV
Week 08Data core

Correlation, probability, and dataset selection

Understand relationships between variables and learn how different datasets support different problems.

  • Learn: correlation matrix, simple probability intuition, target vs features
  • Datasets: Iris, housing, wine overview
  • Output: dataset comparison notes in Colab
Dataset Links Iris CSV sklearn Iris sklearn Wine California Housing
Week 09Similarity + NLP

Text data basics and preprocessing

Move into NLP with tokenization, lowercasing, punctuation removal, stopword awareness, and text cleanup.

  • Learn: basic NLP pipeline from raw sentence to cleaned tokens
  • Dataset: SMS Spam Collection small samples
  • Output: text preprocessing notebook
Dataset Links SMS Spam Collection
Week 10Similarity + NLP

Bag of Words, TF-IDF, and cosine similarity

Represent text numerically and measure how similar two texts are.

  • Learn: CountVectorizer, TF-IDF idea, vectors, dot product intuition, cosine similarity
  • Practice: compare sentences, articles, or titles inside Colab
  • Output: mini text similarity notebook
Dataset Links SMS Spam Collection 20 Newsgroups
Week 11Similarity + NLP

NLP mini applications

Use cosine similarity and vectorization in useful beginner projects like recommendation, matching, and search ranking.

  • Learn: document matching, FAQ matching, basic recommendation logic
  • Dataset: custom notes corpus or small movie/product text dataset
  • Output: text matching mini project
Dataset Links 20 Newsgroups SMS Spam Collection
Week 12Similarity + NLP

Text classification introduction

A gentle bridge from text processing to machine learning using spam or sentiment-style tasks.

  • Learn: train-test split for text, vectorize then classify
  • Datasets: SMS Spam Collection, IMDB subset, or 20 Newsgroups sample
  • Output: first NLP classification notebook
Dataset Links SMS Spam Collection 20 Newsgroups
Week 13ML core

Regression from ground zero

Targets, features, prediction, train-test split, fit, predict, error, and interpretation.

  • Learn: linear regression and evaluation basics
  • Datasets: housing or marks-prediction style dataset
  • Output: regression notebook with charts and findings
Dataset Links California Housing MPG Regression CSV
Week 14ML core

Classification with important benchmark datasets

Modeling category prediction problems with classic datasets that are beginner-friendly and respected.

  • Learn: logistic regression, k-NN, accuracy, confusion matrix
  • Datasets: Iris, Wine, Breast Cancer Wisconsin, Titanic
  • Output: classification comparison notebook
Dataset Links Iris CSV Wine Dataset Breast Cancer Wisconsin Titanic CSV
Week 15ML core

Clustering and unsupervised learning awareness

Introduce grouping without labels and help students understand the difference between supervised and unsupervised learning.

  • Learn: k-means intuition, scaling awareness, cluster interpretation
  • Datasets: Iris features or customer-style dataset
  • Output: clustering notebook with visual interpretation
Dataset Links Iris CSV sklearn Iris
Week 16Capstone

Mini capstone in Google Colab

Choose a path and build something presentable: analysis, prediction, or text similarity / NLP mini app notebook.

  • Project options: analysis dashboard notebook, spam classifier, recommendation / matching notebook, or regression/classification project
  • Deliverables: Colab link, explanation, screenshots, conclusions
  • Output: portfolio-ready final notebook project
Dataset Links Titanic CSV Iris CSV SMS Spam Collection California Housing 20 Newsgroups

Major Libraries, Tools, and IDEs

Important Python, AI-ML, data science, notebook, and coding tools used during the course.

Python + Notebooks

Python
Google Colab
Jupyter Notebook
Anaconda

Core Data Libraries

NumPy
pandas
SciPy
Statsmodels

Charts + Visualization

Matplotlib
Seaborn
Plotly
Bokeh

Machine Learning

scikit-learn
TensorFlow
Keras
PyTorch

NLP Libraries

NLTK
spaCy
Transformers
Gensim

IDEs + Editors

VS Code
PyCharm
Spyder
Learn With Champak Python Editor

Dataset Sources

Kaggle Datasets
UCI ML Repository
sklearn Datasets
Seaborn Data

Developer Tools

GitHub
Git
GitHub Pages
Stack Overflow

Ask Doubts, Queries, or Send Screenshots

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Ready to join the AI-ML journey?

Join the course, complete lessons step by step, ask up to 10 queries per week, and get guidance through WhatsApp and Zoom.

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