Machine Learning from Zero

Machine Learning from Zero: Build Your First Models in Python

Have you ever wondered how a computer can predict a student's result, estimate a value, or classify data?

In this beginner-friendly course, you will learn the foundations of Machine Learning through simple explanations, Python examples, diagrams, and practical exercises.

What you will learn

• Understand what Machine Learning is and how it differs from traditional programming.

• Learn datasets, features, target variables, training data, and testing data.

• Prepare a dataset using Pandas and NumPy.

• Understand regression and classification with real-world examples.

• Build your first Linear Regression and Logistic Regression models.

• Explore K-Nearest Neighbors, Decision Trees, and Random Forests.

• Evaluate predictions using appropriate metrics.

• Complete a student Pass/Fail prediction project.

Who is this course for?

Students, aspiring developers, and beginners who want a practical introduction to Machine Learning using Python.

What do you need?

A computer, basic familiarity with Python, an internet connection, and a willingness to practice. Google Colab can be used to run the examples without installing Python locally.

How this course works

Each topic combines an explanation, an example, practical code, and an exercise. The focus is on understanding the fundamentals rather than memorising code.

Course format: Self-paced beginner lessons, coding demonstrations, quizzes, and a mini-project.

Instructor: Pravin Kumar M

This is an introductory learning course. It does not promise advanced expertise, job placement, or professional certification.