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Keep shoppingComplete Machine Learning for Absolute Beginners
1,039 students
Complete Machine Learning for Absolute Beginners
Learn Machine Learning basics with Python, Scikit-Learn, regression, classification, model training, and real projects
Created byMOHD SAQIB
Last updated 6/2026
English
What you'll learn
- Understand the fundamentals of Machine Learning and how ML systems learn from data using simple real-world examples and beginner-friendly explanations.
- Build your first Machine Learning models in Python using Scikit-Learn, including regression and classification models.
- Learn how to prepare datasets, split data into training and testing sets, and train ML models step-by-step.
- Evaluate machine learning models using metrics like accuracy and make predictions using real datasets.
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Course content
8 sections • 33 lectures • 1h 55m total length
Requirements
- Basic understanding of Python programming is recommended. No prior Machine Learning experience is required. A computer with Python installed is enough to follow along.
Description
Machine Learning is one of the most exciting and in-demand fields in technology today. From recommendation systems to self-driving cars, Machine Learning powers many of the intelligent systems we interact with every day.
This course is designed specifically for absolute beginners who want to understand Machine Learning using Python in a clear and practical way.
You will learn the core concepts of Machine Learning step-by-step without complex mathematics or confusing explanations. The focus of this course is to help you understand how Machine Learning works in real applications and how to build your first ML models using Python.
We begin by understanding what Machine Learning is and how it differs from traditional programming. You will explore the different types of Machine Learning including supervised learning and unsupervised learning.
After that, you will learn how Machine Learning projects actually work in practice. We will prepare datasets, split data into training and testing sets, and use Python libraries such as NumPy, Pandas, and Scikit-Learn to build models.
In this course, you will build your first Machine Learning models including regression and classification models. You will also learn how to evaluate model performance and make predictions using real datasets.Bonus Section Included: Introduction to Deep Learning and Neural Networks.
This course is designed to be beginner-friendly and practical. By the end of the course, you will understand the basic workflow of Machine Learning and gain confidence to explore more advanced topics such as deep learning and AI.
If you already know basic Python and want to take your first step into Machine Learning and Data Science, this course is the perfect place to start.
Who this course is for:
- Beginners who know basic Python and want to start learning Machine Learning, students interested in Data Science, and anyone curious about how ML models work.