Machine Learning Basics Online Course

Machine Learning Basics Online Course

This course takes you through a complete journey in machine learning, starting with core concepts like linear and logistic regression, classification, and statistical learning, before advancing to methods such as cross-validation, bootstrap, and regularization. Through hands-on labs, you’ll practice key algorithms including decision trees, random forests, SVMs, and neural networks, while also exploring CNNs and dimensionality reduction with PCA. The course concludes with deep learning and modern AI applications, covering large language models (LLMs), the OpenAI SDK, and LangChain SDK. By the end, you’ll be equipped to apply both traditional and advanced machine learning techniques to solve real-world problems.

Who should take this course?

This course is perfect for beginners, students, and professionals who want to build a solid foundation in machine learning. It’s ideal for those with little to no prior experience in ML but who are eager to understand core concepts, algorithms, and applications. Whether you’re a student exploring AI, a developer expanding your skill set, or a professional looking to apply machine learning in your field, this course provides the essential starting point.

What you will learn

• Understand core machine learning algorithms like linear and logistic regression
• Master supervised learning techniques such as decision trees and SVM
• Gain hands-on experience building and deploying neural networks
• Explore deep learning concepts like large language models and CNN
• Learn to evaluate machine learning models with classification metrics
• Apply machine learning techniques to real-world data problems

Course Outline

Lectures

• Welcome
• Introduction
• Basics in Statistical Learning
• Linear Regression
• Classification
• Sampling and Bootstrap
• Model Selection
• Going Beyond Linearity
• Tree-Based Methods – Part 1
• Tree-Based Methods – Part 2
• Support Vector Machine (SVM)
• Deep Learning
• Unsupervised Learning
• Classification Metrics

Labs

• Linear Regression
• Logistic Regression
• Ridge
• Decision Tree
• Random Forests
• Support Vector Machine (SVM)
• Multilayer Perceptron (MLP)
• CNN
• PCA
• ROC-AUC

Deep Learning

• Deep Learning – Part 1 – LLM Basics
• Deep Learning – Part 2 – LLM Intermediate
• Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 1
• Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 2
• Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 3
• Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 1
• Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 2
• Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 3