ANU Supervised Machine Learning with Python 5 Units Material Download

Supervised Machine Learning is a cornerstone in modern AI-driven applications. This blog post breaks down the complete unit-wise syllabus for the course "Supervised Machine Learning with Python" — ideal for B.Tech/M.Tech students, aspiring data scientists, and Python enthusiasts.

Whether you're preparing for exams, building your ML portfolio, or starting your journey into artificial intelligence, this post will guide you through each core topic of supervised learning.

UNIT I: Machine Learning Basics

In this introductory unit, you'll learn:

• What is Machine Learning?

• Key ML terminologies and workflows

• Choosing the right algorithm

• Step-by-step process of building an ML model

• Why Python is popular in ML

• Getting started with NumPy (essential for array processing)

• Implementing the k-Nearest Neighbors algorithm

• Working with text data and file parsing

• Data visualization using Matplotlib

• Techniques to normalize numeric values

👉 Outcome: Solid foundation in ML theory and data preparation using Python libraries.

UNIT II: Decision Trees – Splitting Datasets

This unit dives into the classic classification algorithm – Decision Trees.

• Splitting datasets one feature at a time

• Understanding entropy, information gain, and tree construction

• Ensuring dataset consistency

• Implementing recursion to build decision trees

• Plotting decision boundaries using Matplotlib

👉 Outcome: Master tree-based logic for classification and visual understanding of decisions.

UNIT III: Naïve Bayes Classifier – Probability-based Learning

Explore probabilistic learning techniques using the Naïve Bayes algorithm.

• Concepts of probability distributions

• How Naïve Bayes handles classification

• Learning from RSS feed data (real-world text classification)

• Revealing insights through Bayesian inference

👉 Outcome: Ability to perform spam filtering, sentiment analysis, and other probabilistic tasks.

UNIT IV: Logistic Regression & Optimization

Understand one of the most widely used classification models in ML:

• Logistic Regression and the Sigmoid function

• Gradient Descent and optimization techniques    ( two seprate links) 

• Dealing with missing data

• Real-world use cases like binary classification and click prediction

👉 Outcome: Implement regression models that handle binary outcomes effectively.

UNIT V: Support Vector Machines (SVM)

Finish the course with a powerful classifier: Support Vector Machines.

• Introduction to SVM and its mathematical intuition

• Understanding the SMO algorithm

• Using kernels for non-linear data separation

• Comparing SVM with other models

👉 Outcome: Master a top-tier model used in image classification, face recognition, and more.

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