🎓 AI Vidya 3-Month AI & Data Science Basics Program
- AI Vidya
- Nov 5
- 2 min read
Updated: Nov 6
From Zero to Foundation in Python, Data, and Machine Learning
🧭 Program Overview
AI Vidya 3-Month AI & Data Science Basics Program
Duration | 3 Months (12 Weeks) |
Level | Beginner |
Mode | Mentor-led + Self-paced Projects |
Tools | Python, Jupyter, Pandas, Matplotlib, Scikit-learn |
Outcome | Foundational AI Certificate + Portfolio Projects |
🔹 Month 1: Python Programming & Data Handling
Goal: Build a strong base in Python and understand how to handle data.
Week 1 – Python Fundamentals
Introduction to Python & Jupyter Notebook
Data types, variables, loops, and functions
Conditional statements and debugging
Mini Project: Basic Calculator & Data Entry App
Week 2 – Working with Data
NumPy and Pandas for data manipulation
Importing CSV/Excel data, cleaning and filtering
Handling missing data and basic transformations
Hands-On: Explore real-world datasets (Sales / Movies / IPL)
Week 3 – Data Visualization
Introduction to Matplotlib & Seaborn
Creating bar, line, scatter, and pie charts
Customizing visuals and creating dashboards
Mini Project: IPL or COVID-19 Data Dashboard
Week 4 – Exploratory Data Analysis (EDA)
What is EDA and why it matters in AI
Detecting patterns and correlations
Summarizing insights from data
Project: Data Storytelling — Present findings visually
🔹 Month 2: Statistics, Probability & Introduction to ML
Goal: Understand data behavior and basic machine learning concepts.
Week 5 – Statistics for Data Science
Mean, median, mode, variance, standard deviation
Probability, distribution, and z-scores
Correlation and covariance
Hands-On: Statistical summary of a dataset
Week 6 – Linear Algebra & ML Concepts
Vectors, matrices, dot product, and normalization
Introduction to machine learning & datasets
Supervised vs unsupervised learning
Mini Project: Predict house prices with Linear Regression
Week 7 – Classification Algorithms
Logistic Regression basics
Decision Trees and KNN introduction
Model training, testing, and evaluation (accuracy, F1-score)
Project: Email Spam Classifier / Student Performance Predictor
Week 8 – Model Optimization & Evaluation
Feature scaling, encoding, normalization
Cross-validation and train-test split
Understanding bias, variance, and overfitting
Assignment: Compare models for same dataset
🔹 Month 3: Real-World Application & Capstone
Goal: Apply everything learned to a real dataset and create your first AI project.
Week 9 – Data Project Design
How to plan and structure a data project
Choosing datasets from Kaggle / UCI
Setting up data cleaning and analysis workflow
Mentor Review: Project Proposal
Week 10 – Building ML Pipelines
Automating workflow: EDA → Training → Prediction
Saving and reusing models (Pickle, Joblib)
Hands-On: Create an end-to-end ML notebook
Week 11 – Model Deployment Basics
Introduction to Streamlit for quick deployment
Building a simple AI web app
Mini Project: Deploy a Predictive Dashboard
Week 12 – Capstone Project & Career Prep
Capstone Options (Choose One):
Predictive Sales Forecast
HR Attrition Prediction
Movie Recommendation Mini-System
EDA Report on Public Dataset
🧩 Program Deliverables
6+ Mini Projects
1 Capstone Project
GitHub Portfolio Setup
Resume + LinkedIn Review Session
AI Vidya Certificate of Completion
💼 Career Outcomes
6+ Mini Projects
1 Capstone Project
GitHub Portfolio Setup
Resume + LinkedIn Review Session
AI Vidya Certificate of Completion
💬 Mentorship & Ecosystem
Weekly mentor-led live sessions
Community discussions on Discord
Project feedback and portfolio guidance
🧠 Tools Covered
Python
Jupyter Notebook
Pandas, NumPy
Matplotlib, Seaborn
Scikit-learn
Streamlit
Your gateway to AI and Data Science — in just 3 months.
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