Public

100 Days of Machine Learning

Comprehensive intermediate ML journey covering algorithms, model evaluation, and real-world applications. Progress from foundational concepts through advanced techniques with hands-on practice.

Level: intermediateCreated: 1/23/2026

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

Prerequisites

•Python programming
•Linear algebra basics
•Statistics fundamentals
•Data manipulation skills

Learning Outcomes

✓Implement and evaluate supervised learning models
✓Apply unsupervised learning techniques effectively
✓Optimize models using advanced techniques
✓Build end-to-end ML pipelines
✓Deploy and monitor ML systems

Weekly Breakdown

Week Goals

• Refresh ML concepts
• Setup development environment

Day 1

ML Basics Refresh

Review supervised, unsupervised, and reinforcement learning paradigms.

Tasks

→Review ML taxonomy
→Outline project ideas

Day 2

Environment Setup

Configure tools for ML development.

Tasks

→Install Scikit-learn, TensorFlow, Pandas
→Setup Jupyter notebook

Day 3

Data Pipeline Basics

Understand data loading and preprocessing steps.

Tasks

→Load CSV datasets
→Explore data distributions

Day 4

Feature Engineering

Learn to extract and transform relevant features.

Tasks

→Handle missing values
→Normalize features

Day 5

Train-Test Split

Master data partitioning for model evaluation.

Tasks

→Split datasets properly
→Understand cross-validation

Day 6

Evaluation Metrics

Learn accuracy, precision, recall, F1, and AUC metrics.

Tasks

→Calculate classification metrics
→Interpret confusion matrix

Day 7

Model Selection

Understand bias-variance tradeoff and model selection.

Tasks

→Compare multiple algorithms
→Analyze performance trade-offs