Fundamentals of Machine Learning

Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a comprehensive foundation in machine learning, taking you through both the theoretical and practical aspects of this powerful field. By learning the fundamentals of algorithms, models, and techniques, you will gain the skills to design, implement, and assess machine learning systems effectively. Throughout the course, you'll dive deep into various methods, including regression, classification, decision trees, SVM, deep learning, and more. The course is structured into lectures, hands-on labs, and deep learning-focused modules. It starts with foundational concepts such as statistical learning and progresses to complex models like neural networks and support vector machines. You'll also explore practical tools like Principal Component Analysis (PCA), random forests, and classification metrics, helping you build confidence in both theory and application. Ideal for those new to the field of machine learning, the course assumes no prior experience in programming or data science. However, a basic understanding of algebra and statistics will be beneficial. It's designed for learners at all levels, providing an accessible entry point into machine learning while offering deep technical insights for more experienced students. By the end of the course, you will be able to implement machine learning models, use deep learning techniques, assess model performance, and apply machine learning methods to real-world datasets.

Syllabus

Lectures

In this module, we will explore the foundational principles of machine learning, from the basics of statistical learning to advanced techniques like decision trees and deep learning. You will learn essential concepts such as linear regression, classification, and the importance of model selection to prevent overfitting. By the end, you will gain a comprehensive understanding of how machine learning works and the tools used to build robust models.

Labs

In this module, we will dive into hands-on labs where you will apply theoretical knowledge to solve real-world machine learning problems. You will work with popular algorithms such as linear regression, SVM, and decision trees, experimenting with techniques like PCA for data reduction and building deep learning models like CNNs. By the end, you will be able to build and fine-tune machine learning models to handle diverse datasets.

Deep Learning

In this module, we will focus on deep learning, specifically Large Language Models (LLMs), and their applications. You will gain practical experience with powerful SDKs like OpenAI and LangChain, learning to build and optimize LLM agents for real-world scenarios. By the end of the module, you will have a deeper understanding of LLMs and be equipped to deploy them effectively using advanced tools and techniques.