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100 Days of Machine Learning Fundamentals

A comprehensive 100-day journey from ML basics to advanced applications. Master supervised/unsupervised learning, model evaluation, and build a real-world capstone project.

Level: beginnerCreated: 1/23/2026

This plan is public and visible in the Learning Plans Library.

Prerequisites

•Python programming basics
•Linear algebra fundamentals
•Statistics and probability

Learning Outcomes

✓Understand core ML algorithms and theory
✓Build and evaluate predictive models
✓Handle data preprocessing and feature engineering
✓Implement both supervised and unsupervised learning
✓Deploy ML models for real-world problems

Weekly Breakdown

Week Goals

• Understand ML concepts
• Set up development environment

Day 1

What is ML

Introduction to machine learning, types, and applications.

Tasks

→Define supervised vs unsupervised learning
→Explore real-world ML examples

Day 2

ML Workflow

Learn the standard steps in any ML project.

Tasks

→Study problem definition to deployment
→Map workflow stages

Day 3

Python Setup

Configure Python environment with ML libraries.

Tasks

→Install NumPy, Pandas, Scikit-learn
→Test imports and versions

Day 4

NumPy Essentials

Master NumPy arrays and operations.

Tasks

→Practice array creation and manipulation
→Learn vectorization benefits

Day 5

Pandas Basics

Learn data manipulation with Pandas DataFrames.

Tasks

→Create and explore DataFrames
→Practice filtering and selection

Day 6

Data Exploration

Analyze datasets to understand patterns.

Tasks

→Load sample dataset
→Compute statistics and distributions

Day 7

Visualization Intro

Create plots to visualize data patterns.

Tasks

→Use Matplotlib for basic plots
→Create histograms and scatter plots