Skip to content

📅 Day 01 — Part 1: Machine Learning Basics

Goal: Understand what machine learning is, how ML projects are structured, and how to train your first model safely.

Roadmap

# Topic File
1 What is Machine Learning? 01-what-is-machine-learning.md
2 Supervised vs Unsupervised Learning 02-supervised-unsupervised.md
3 Train/Test Split and Leakage 03-train-test-split-and-leakage.md
4 Scikit-learn Workflow 04-scikit-learn-workflow.md
5 Exercises 05-exercises.md

Learning Objectives

  • [ ] Explain ML in plain English
  • [ ] Distinguish supervised, unsupervised, and reinforcement learning
  • [ ] Identify features and target variables
  • [ ] Split data into train and test sets
  • [ ] Avoid basic data leakage
  • [ ] Train a first model with scikit-learn

Prerequisites

  • Python basics
  • NumPy and Pandas
  • EDA workflow
  • Basic statistics

Setup

pip install scikit-learn pandas numpy matplotlib seaborn

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

➡️ Start with 01-what-is-machine-learning