Machine Learning : Basics to Advanced 2026

Learn Machine Learning from scratch using Python — covering , data handling, popular algorithms, and real-world project

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.

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 ForStudents who want to start a career in Machine LearningBeginners with basic Python knowledgeAspiring Data Scientists and ML EngineersSoftware developers who want to add ML skillsAnyone preparing for internships, jobs, or interviews in MLMachine Learning Algorithms CoveredYou will learn and implement the following algorithms with hands-on projects:Linear RegressionMultiple Linear RegressionPolynomial RegressionLogistic RegressionK-Nearest Neighbors (KNN)Support Vector Machine (SVM)Naive BayesDecision TreeRandom ForestK-Means ClusteringWhat You Will LearnComplete Machine Learning workflow Data preprocessing, feature engineering, and exploratory data analysis (EDA)Model training, testing, validation, and performance evaluationHow to choose the right algorithm for a given problemPrerequisitesBasic understanding of Python programmingWillingness to learn mathematics behind ML (explained simply)No prior experience in Machine Learning or Data Science required.Career OutcomesAfter completing this course, you will be confident to:Build Machine Learning models from scratchCrack internships and entry-level ML rolesApply ML to real-world business problemsMove 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