Machine Learning Basics
Programming · 9 chapters · 103 sections · about 41 hours
Chapters
9 chapters · 103 sections- 1What is Machine Learning?8 sections
- 2Types of Machine Learning22 sections
- 3Understanding NumPy for Machine Learning9 sections
- 4Understanding Pandas for Machine Learning14 sections
- 5Data Preprocessing for Machine Learning7 sections
- 6Supervised Learning – Linear Regression11 sections
- 7Supervised Learning – Logistic Regression12 sections
- 8Model Evaluation Metrics9 sections
- 9End-to-End Machine Learning Project – Predicting Student Exam Performance11 sections
About this course
This course is a beginner-friendly introduction to the world of Machine Learning (ML). It’s designed for students, beginners, and curious minds who want to understand how machines learn from data. You don’t need to be a math expert or a coder — just basic Python knowledge and a willingness to learn. In this course, you’ll: Understand what machine learning is (in simple words) Explore different types of machine learning Learn how to work with data Build your first machine learning models using Python Use real-world examples to make things clear Apply what you learn in a final hands-on mini project By the end, you’ll be able to confidently explain ML concepts, build small ML programs, and understand how companies like Netflix or Google use ML in real life.