A Self-Learning Roadmap to Machine Learning

📌 Overview

This roadmap is designed for students starting Machine Learning from scratch.
It is split into two phases:

• Phase 1: Foundations (Math + Python)
• Phase 2: ML Concepts + Practice + Mini Projects

Phase 1: Machine Learning Foundations

1) Math for Machine Learning

Before starting ML, students should be comfortable with the following topics.

✅ Topics to Learn

Linear Algebra

• Vectors, matrices
• Eigenvalues, eigenvectors

Calculus & Gradients

• Derivatives
• Chain rule
• Gradients

Probability & Statistics

• Distributions
• Expectation
• Mean, variance

⭐ Best Resources

Linear Algebra

• 3Blue1Brown Playlist

Calculus

• 3Blue1Brown Playlist

Probability and Statistics

• StatQuest Playlist

🎓 Courses (Optional but Recommended)

• Coursera: Mathematics for Machine Learning
• Scaler: Math for ML (Free)
• LinkedIn Learning Path

2) Python for Data Science

Students should become confident in using Python for data handling and visualization.

✅ Skills Students Must Learn

• NumPy — numerical computing
• Pandas — data manipulation
• Matplotlib / Seaborn — visualization
• scikit-learn — ML models (after ML basics)
• PyTorch — deep learning (after DL basics)

⭐ Resources

• Data Analysis with Python (YouTube Course)
• NumPy + Pandas + Matplotlib GitHub
• Kaggle Python
• Probability and Statistics Notebook

Phase 2: Machine Learning Concepts + Practice

1) ML Core Concepts

Students should clearly understand:

• Features (X) and Target (y)
• Train vs Test split
• Overfitting vs Underfitting

2) Models to Learn (Beginner Level)

Start with these 3 models:

• Linear Regression
• Logistic Regression
• Decision Trees

Also learn the basics of:

Representation → Loss Function → Optimization

3) Evaluation Metrics

Regression Metrics

• MAE
• MSE
• RMSE

Classification Metrics

• Accuracy
• Confusion Matrix
• Precision, Recall, F1-score

📚 Resources for Phase 2

Theory Resources

• Google ML Crash Course
• StatQuest
• Machine Learning Mastery
• Coursera: Machine Learning

Hands-on Resources

• Kaggle Intro to ML
• Python Data Science Handbook
• Microsoft ML for Beginners
• YouTube ML Playlist

🧪 Mini Projects (Choose Any 2)

Students must complete any 2 projects from the list below.

Option A: House Price Prediction (Regression)

Dataset:

• House Prices Kaggle Dataset

Deliverables:

• Preprocessing
• Model training
• RMSE evaluation
• Conclusion

Option B: Titanic Survival Prediction (Classification)

Dataset:

• Titanic Kaggle Dataset

Deliverables:

• Encoding + preprocessing
• Model training
• Confusion matrix
• Precision/Recall

Option C: Student Performance Prediction

Dataset:

• Students Performance Dataset

Deliverables:

• EDA
• Correlation analysis
• Model training
• Evaluation

Option D: Diabetes Prediction

Dataset:

• Pima Indians Diabetes Dataset

Deliverables:

• Classification model
• F1-score evaluation
• Conclusion

📦 Final Submission Format (Phase 1 + Phase 2)

Students must submit one notebook/report containing:

• Dataset loading
• Data cleaning + missing value handling
• Exploratory Data Analysis (EDA) + plots
• Feature engineering (basic)
• Model training
• Evaluation metrics
• Final conclusion (5–10 lines)

✅ Outcome

By the end of Phase 1 and Phase 2, students will be able to:

• Understand ML fundamentals clearly
• Build beginner ML models using scikit-learn
• Evaluate models properly
• Complete 2 end-to-end mini projects
• Write a clean ML notebook/report for submission