Your cart is empty.
Keep shopping
Machine Learning : Basics to Advanced 2026
3 students
Machine Learning : Basics to Advanced 2026
Learn Machine Learning from scratch using Python — covering , data handling, popular algorithms, and real-world project
Created byVishal Vishal
Last updated 3/2026
English
What you'll learn
- Understand the complete Machine Learning workflow, from data collection and preprocessing to model training and evaluation
- Implement and clearly understand Regression algorithms including Linear, Multiple Linear, and Polynomial Regression.
- Implement and clearly understand Regression algorithms including Linear, Multiple Linear, and Polynomial Regression.
- Apply Classification algorithms such as Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, and Random Forest to solve practical problems.
- Work with Unsupervised Learning techniques, including K-Means Clustering for pattern discovery and customer segmentation.
- Evaluate and improve model performance using techniques like train-test split, cross-validation, and performance metrics.
Explore related topics
Course content
9 sections • 52 lectures • 6h 25m total length
Requirements
- Basic understanding of Python programming (variables, loops, functions).
- No prior experience in Machine Learning, Data Science, or AI is required
- Basic school-level mathematics is enough (all required math concepts are explained simply)
- A computer or laptop with internet access
Description
This course contains the use of artificial intelligence.
Machine Learning: Basics to Advanced (2026) is a complete, structured, and practical course designed to help you master Machine Learning using Python. This course starts from absolute fundamentals and gradually moves toward advanced algorithms and real-world applications. A basic knowledge of Python is required, but no prior Machine Learning experience is needed.
This course is designed in a simple and beginner-friendly way so that even students with no background in Machine Learning can understand concepts clearly and confidently apply them in real projects.
Who This Course Is For
Students who want to start a career in Machine Learning
Beginners with basic Python knowledge
Aspiring Data Scientists and ML Engineers
Software developers who want to add ML skills
Anyone preparing for internships, jobs, or interviews in ML
Machine Learning Algorithms Covered
You will learn and implement the following algorithms with hands-on projects:
Linear Regression
Multiple Linear Regression
Polynomial Regression
Logistic Regression
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Naive Bayes
Decision Tree
Random Forest
K-Means Clustering
What You Will Learn
Complete Machine Learning workflow
Data preprocessing, feature engineering, and exploratory data analysis (EDA)
Model training, testing, validation, and performance evaluation
How to choose the right algorithm for a given problem
Prerequisites
Basic understanding of Python programming
Willingness to learn mathematics behind ML (explained simply)
No prior experience in Machine Learning or Data Science required.
Career Outcomes
After completing this course, you will be confident to:
Build Machine Learning models from scratch
Crack internships and entry-level ML roles
Apply ML to real-world business problems
Move forward toward Advanced AI and Deep Learning.
Disclosure:
This course uses AI-generated images and visual content for better explanation and presentation. The instructor’s own voice, knowledge, and teaching methods are used throughout the course.
Who this course is for:
- Students who want to start a career in Machine Learning or Data Science
- Software developers who want to add Machine Learning skills to their profile
- Anyone interested in learning how machines learn from data