Machine Learning with Python: From Basics to Capstone

Curriculum

12 Sections
49 Lessons
10 Weeks

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Module 1: Introduction to Machine Learning
Give students a clear foundation in what Machine Learning is, how it differs from related fields, and where it’s applied in real life.
6
Module 2: Python Essentials for ML
5
Module 3: Data Wrangling & Visualization, starting with:
5
Module 4: Data Preprocessing
5
Module 5: Regression Algorithms!
This module introduces the foundational concept of regression in machine learning — perfect for prediction tasks like house prices, salaries, etc.
5
Module 6: Classification Algorithms
5
Module 7: Decision Trees
6
Module 8: Model Evaluation & Tuning
5
Module 9: Unsupervised Learning
5
Module 10: Dimensionality Reduction
5
Module 11: Capstone Project Preparation
5
Module 12: Capstone Project Execution
5

Lesson 3: Model Evaluation – MAE, MSE, R² Explained

📏 Evaluating Regression Models – MAE, MSE, R²

Table of Contents

Building a model is only half the work — you must also evaluate its accuracy and reliability. In this lesson, we’ll cover the most used evaluation metrics in regression.

📊 1. MAE – Mean Absolute Error

Average of absolute differences between predicted and actual values. Lower MAE = better model.

from sklearn.metrics import mean_absolute_error

📊 2. MSE & RMSE – Mean Squared Error & Root MSE

Measures average squared error. RMSE is square root of MSE. Sensitive to large errors.

from sklearn.metrics import mean_squared_error

📊 3. R² Score – Coefficient of Determination

Indicates how well data fits the model. R² = 1 means perfect fit, R² = 0 means poor fit.

from sklearn.metrics import r2_score

⏭️ Next: Mini Project – Predict Housing Prices or Salary

Lesson 2: Linear Regression – Theory + Implementation