Introduction to Machine Learning: Supervised Learning

Go to Course: https://www.coursera.org/learn/introduction-to-machine-learning-supervised-learning

Introduction

## Course Review: Introduction to Machine Learning: Supervised Learning on Coursera ### Overview The course "Introduction to Machine Learning: Supervised Learning" offered on Coursera is a comprehensive exploration into the world of supervised machine learning algorithms and their applications. With a strong focus on practical skills, the course equips learners with the necessary knowledge and hands-on experience to implement a variety of machine learning models using Python, making it an excellent choice for anyone looking to delve into the field of data science and machine learning. ### Course Structure and Content This course is structured in a way that gradually builds your understanding from basic principles to more complex methods. Here's a breakdown of the key topics covered: 1. **Introduction to Machine Learning & Linear Regression**: The course kicks off by establishing foundational concepts in machine learning and introduces linear regression as a starting point. Learners are guided through data cleaning and exploratory data analysis (EDA), crucial steps in any machine learning project. 2. **Multilinear Regression**: Building on linear regression, this section explores how to handle multiple explanatory variables. You will gain insights into interpreting coefficients and visualizing data in 3D, which is an essential skill for complex data sets. 3. **Logistic Regression**: The course shifts focus from regression to classification tasks with logistic regression. Real-world applications such as predicting health outcomes are introduced, fostering a practical understanding of this foundational tool in data science. 4. **Non-parametric Models**: This module emphasizes intuitive models like k-Nearest Neighbors and decision trees. Through labs, you will develop classifiers using decision trees and KNN, reinforcing the importance of addressing overfitting through strategies like pruning. 5. **Ensemble Methods**: Moving into more advanced territory, this module covers ensemble techniques such as random forests and boosting methods. You will learn how to mitigate overfitting and enhance model performance through aggregation techniques, equipping you with skills that are highly valued in machine learning competitions. 6. **Kernel Methods**: The course wraps up with a deep dive into Support Vector Machines (SVMs). Here, you’ll demystify complex topics such as margin types and hyperparameter tuning while preparing to finalize your project. ### Practical Applications One of the highlights of the course is its emphasis on practical application. Each week consists of hands-on labs where you can apply what you've learned to real-world datasets. This experiential learning is invaluable, as it enhances theoretical understanding while providing critical coding practice in Python, which is essential for any aspiring data scientist. ### Final Project A significant aspect of the course is the capstone project, which serves as a culmination of your learned skills. You will select a dataset, perform EDA, define a problem, and develop a model to solve it. This project is a fantastic opportunity to showcase your ability to implement machine learning techniques, and the structured feedback from peers helps refine your work further. ### Recommendations - **Who Should Take This Course?**: This course is ideal for individuals with some prior coding experience, particularly in Python, who are eager to expand their understanding of machine learning. If you're a beginner or someone looking to build upon your foundational knowledge in statistics and programming, this course serves as a solid stepping stone. - **What Will You Gain?**: By the end of the course, participants will walk away with several key competencies, including the ability to implement various supervised learning models, an understanding of when to employ different algorithms based on data type, and practical experience with data cleaning and project-based problem-solving. ### Conclusion In conclusion, "Introduction to Machine Learning: Supervised Learning" is an exceptional course that successfully merges theoretical insights with practical skills. It's designed for those looking to navigate the complexities of machine learning with confidence. Whether you're aiming to enhance your resume, pivot your career towards data science, or simply expand your knowledge, I wholeheartedly recommend enrolling in this course on Coursera. It's engaging, informative, and a crucial stepping stone in the expansive field of machine learning.

Syllabus

Introduction to Machine Learning, Linear Regression

This week, we will build our supervised machine learning foundation. Data cleaning and EDA might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression before. With the buzz around machine learning, perhaps it seems surprising that we are starting with such a standard statistical technique. In "How Not to Be Wrong: The Power of Mathematical Thinking", Jordan Ellenberg refers to linear regression as "the statistical technique that is to social science as the screwdriver is to home repair. It's the one tool you're pretty much going to use, whatever the task" (51). Linear regression is an excellent starting place for solving problems with a continuous outcome. Hopefully, this week will help you appreciate how much you can accomplish with a simple model like this.

This week we are building on last week's foundation and working with more complex linear regression models. After this week, you will be able to create linear models with several explanatory and categorical variables. Mathematically and syntactically, multiple linear regression models are a natural extension of the simpler linear regression models we learned last week. One of the differences that we must keep in mind this week is that our data space is now 3D instead of 2D. The difference between 3D and 2D has implications when considering how to do things like creating meaningful visualizations. It is essential to understand how to interpret coefficients. Machine learning involves strategically iterating and improving upon a model. In this week's lab and Peer Review, you will identify weaknesses with linear regression models and strategically improve on them. Hopefully, as you progress through this course specialization, you will get better and better at this iterative process.

Even though the name logistic regression might suggest otherwise, we will be shifting our attention from regression tasks to classification tasks this week. Logistic regression is a particular case of a generalized linear model. Like linear regression, logistic regression is a widely used statistical tool and one of the foundational tools for your data science toolkit. There are many real-world applications for classification tasks, including the financial and biomedical realms. In this week's lab, you will see how this classic algorithm will help you predict whether a biopsy slide from the famous Wisconsin Breast Cancer dataset shows a benign or malignant mass. We also advise starting the final project that you will turn in Week 7 of the course this week. This week, find a project dataset, start performing EDA and define your problem. Use the project rubric as a guide, and don't be afraid to look at a few datasets until you find one well-suited to the project.

This week we will learn about non-parametric models. k-Nearest Neighbors makes sense on an intuitive level. Decision trees are a supervised learning model that can be used for either regression or classification tasks. In Module 2, we learned about the bias-variance tradeoff, and we've kept that tradeoff in mind as we've moved through the course. Highly flexible tree models have the benefit that they can capture complex, non-linear relationships. However, they are prone to overfitting. This week and next, we will explore strategies like pruning to avoid overfitting with tree-based models. In this week's lab, you will make a KNN classifier for the famous MNIST dataset and then build a spam classifier using a decision tree model. This week we will once again appreciate the power of simple, understandable models. Keep going with your final project. Once you've finalized your dataset and EDA, start on the initial approach for your main supervised learning task. Review the course material, read research papers, look at GitHub repositories and Medium articles to understand your topic and plan your approach.

Last week, we learned about tree models. Despite all of the benefits of tree models, they had some weaknesses that were difficult to overcome. This week we will learn about ensembling methods to overcome tree models' tendency to overfit. The winner utilizes an ensemble approach in many machine learning competitions, aggregating predictions from multiple tree models. This week you will start by learning about random forests and bagging, a technique that involves training the same algorithm with different subset samples of the training data. Then you will learn about boosting, an ensemble method where models train sequentially. You will learn about two essential boosting algorithms: AdaBoost and Gradient Boosting. This week, work on the main analysis of your final project. Iterate and improve on your models. Compare different models. Perform hyperparameter optimization. Sometimes this part of a machine learning project can feel tedious, but hopefully, it will be rewarding to see your performance improve.

This week we will be exploring another advanced topic, Support Vector Machines. Don't let the name intimidate you. This week, we will work through understanding this powerful supervised learning model. Hopefully, you will build an intuitive understanding of essential concepts like the difference between hard and soft margins, the kernel trick, and hyperparameter tuning. Next week, you will submit the three deliverables for your final project: the report, video presentation, and a link to your GitHub repository. Suppose you aim to finish iterating on your models, hyperparameter optimization, etc., this week. In that case, next week, you can polish your report, make sure your GitHub repository is ready for Peer Review, and give an excellent presentation of your work.