Chapter 1: Introduction to Applied Machine Learning

Welcome to the exciting realm of applied machine learning using Python! This course serves as your gateway to understanding how to enable computers to learn from data. Whether you are an aspiring data scientist, someone interested in data, or involved in a data-centric profession, this course will be your roadmap. We will cover foundational concepts and guide you through the essential principles. You’ll become adept at using Python, a widely-used programming language in data science, and learn to construct intelligent systems capable of performing remarkable tasks with data. Get ready for an engaging journey where we transform concepts into practical tools, without getting bogged down in theoretical complexities.

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If you’re eager to sharpen your skills in this domain, the University of Michigan is providing a course on Coursera that you can audit at no cost.

Course Details: Intermediate level, approximately 31 hours, flexible schedule, rated 4.6 stars with 8,382 reviews.

Syllabus Overview

Module 1: Fundamentals of Machine Learning and Introduction to SciKit-Learn

In this initial module, you will embark on your exploration of fundamental machine learning concepts, tasks, and workflows, specifically through a classification lens. The K-nearest neighbors method will be utilized, with practical implementation via the SciKit-Learn library.

Module 2: Supervised Machine Learning - Part 1

This segment delves into various supervised learning methods applicable for classification and prediction tasks. You will investigate the relationship between model complexity and prediction accuracy, emphasizing the significance of feature scaling. Techniques such as regularization to mitigate overfitting will be introduced. The module encompasses linear regression (covering least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, cross-validation for model assessment, and decision trees. Prepare for an in-depth exploration of supervised learning methods!

The first video titled "Applied Data Science with Python, University of Michigan Specialization on Coursera: Full Review!" offers a comprehensive overview of the course, detailing the learning path and objectives.

Module 3: Evaluation

In this module, you will focus on evaluation techniques and model selection strategies to enhance the understanding and performance of your machine learning models.

Module 4: Supervised Machine Learning - Part 2

Here, advanced supervised learning methods will be discussed, including ensemble techniques like random forests and gradient-boosted trees. You will also explore neural networks, with an optional overview of deep learning. Additionally, the crucial concept of data leakage in machine learning will be addressed, alongside methods for its detection and prevention. This is your chance to delve deeper into powerful tools while learning to avoid common pitfalls.

The course is instructed by Kevyn Collins-Thompson, an Associate Professor of Information and Computer Science at the University of Michigan.

You can enroll in this course here.

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Additional Resources

Module 2: Supervised Machine Learning - Complete Course

The second video titled "Applied Machine Learning in Python Complete Course" provides a thorough guide to mastering machine learning techniques and methodologies.

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