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Machine Learning with Python: From Basics to Capstone

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

Machine Learning Lifecycle – From Data to Deployment

🔁 Lesson 4: Machine Learning Lifecycle – From Data to Deployment

Every Machine Learning project follows a structured path — from understanding the problem to deploying the final model. In this lesson, you’ll learn the **7 key steps** of the ML lifecycle.

1️⃣ Problem Definition

Understand the business problem. What do we want to predict, classify, or optimize?

2️⃣ Data Collection

Gather raw data from sources like CSV files, databases, APIs, or sensors.

3️⃣ Data Cleaning

Handle missing values, duplicates, outliers, and format inconsistencies.

4️⃣ Feature Engineering

Create new features or transform existing ones to improve model performance.

5️⃣ Model Building

Choose an algorithm and train your model on the prepared data.

6️⃣ Model Evaluation

Test model accuracy using metrics like MAE, MSE, Accuracy, Precision, F1-score.

7️⃣ Deployment

Deploy the model into a real-world application via API or dashboard.

📌 Summary

ML is not just about building models — it’s about solving problems, cleaning data, evaluating results, and putting the model to use. Mastering the ML lifecycle is key to real-world success.

📝 Self-Check: Can You Answer?

What comes first in the ML lifecycle: cleaning or collecting?
Which step involves creating new variables from existing ones?
How do you measure if a model is “good”?

👉 Up next: We begin with Python basics for ML — variables, loops, and functions in action.

AI vs ML vs DL – What’s the Difference?

Quiz: AI vs ML vs DL + Machine Learning Lifecycle