Supervised Machine Learning: Classification

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

Introduction

### Course Review: Supervised Machine Learning: Classification on Coursera If you’re aiming to build a solid foundation in supervised machine learning, particularly in the realm of classification, then the **Supervised Machine Learning: Classification** course on Coursera is an excellent choice. This course stands out not only for its structured approach to essential concepts of classification but also for its hands-on learning methodology. #### Course Overview This course introduces learners to one of the pivotal aspects of supervised Machine Learning—classification. Designed for both beginners and those with some background in machine learning, the curriculum equips you with the skills to train predictive models that classify categorical outcomes effectively. You’ll gain an understanding of various error metrics to compare models, which is crucial in the data-driven decision-making process. By the end of the course, you'll be capable of: - Differentiate between various classification algorithms. - Implement classification models effectively with practical examples. - Analyze model performance using error metrics. - Handle datasets with unbalanced classes through proven techniques. #### Syllabus Breakdown The course is organized into several modules, each delving into fundamental classification algorithms, which are essential for anyone looking to excel in this domain. 1. **Logistic Regression**: - As one of the most commonly used classification algorithms, the module on logistic regression makes for a thoughtful starting point. Here, you’ll extend a linear regression model into logistic regression, understanding its interpretability and application in various fields, especially finance. 2. **K Nearest Neighbors (KNN)**: - This module introduces the straightforward yet powerful KNN algorithm. Through theory and demos, you will practice building models using this approachable technique, gaining insights into its computation and interpretation. 3. **Support Vector Machines (SVM)**: - SVMs serve as a foundation in the classification toolkit. This module emphasizes how hyperplanes are utilized to segregate data points into distinct classes, enriching your understanding of SVMs not just for classification, but also for their versatility in regression and outlier detection. 4. **Decision Trees**: - Renowned for their visual and interpretative capabilities, decision trees are introduced with an exploration of their advantages and disadvantages. This module includes hands-on examples that demystify how to create decision tree models. 5. **Ensemble Models**: - The course then transitions to ensemble methods, which enhance model reliability and performance. This module covers tree-based ensembles and popular techniques like stochastic gradient boosting, helping you to appreciate how ensemble methods can improve predictive performance. 6. **Modeling Unbalanced Classes**: - Finally, this critical module tackles the issue of unbalanced datasets that often plague classification tasks. Essential techniques such as stratified sampling are discussed, along with novel approaches to ensure your classifiers are robust. #### Hands-On Learning and Practical Application What sets this course apart is its emphasis on hands-on learning. Each module includes practical exercises where you can apply theoretical concepts using Python libraries like sklearn. This real-world application reinforces learning and helps develop valuable skills for your portfolio. #### Recommendations I wholeheartedly recommend the **Supervised Machine Learning: Classification** course for anyone interested in applied machine learning. Whether you’re a student aiming to enter the tech industry or a professional looking to enhance your skill set, this course is invaluable. The structured content, combined with practical exercises and the opportunity to learn from experts in the field, makes it a compelling resource. Furthermore, the skillset you acquire from this course is not only theoretical but immensely applicable in solving real-world problems. Mastering classification techniques can serve as a powerful tool in various industries—be it finance, healthcare, marketing, or technology. In conclusion, if you are eager to delve into the world of supervised machine learning and classification, enrolling in this course on Coursera will undoubtedly set you on the right path to success.

Syllabus

Logistic Regression

Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.

K Nearest Neighbors is a popular classification method because they are easy computation and easy to interpret. This module walks you through the theory behind k nearest neighbors as well as a demo for you to practice building k nearest neighbors models with sklearn.

This module will walk you through the main idea of how support vector machines construct hyperplanes to map your data into regions that concentrate a majority of data points of a certain class. Although support vector machines are widely used for regression, outlier detection, and classification, this module will focus on the latter.

Decision tree methods are a common baseline model for classification tasks due to their visual appeal and high interpretability. This module walks you through the theory behind decision trees and a few hands-on examples of building decision tree models for classification. You will realize the main pros and cons of these techniques. This background will be useful when you are presented with decision tree ensembles in the next module.

Ensemble models are a very popular technique as they can assist your models be more resistant to outliers and have better chances at generalizing with future data. They also gained popularity after several ensembles helped people win prediction competitions. Recently, stochastic gradient boosting became a go-to candidate model for many data scientists. This model walks you through the theory behind ensemble models and popular tree-based ensembles.

Some classification models are better suited than others to outliers, low occurrence of a class, or rare events. The most common methods to add robustness to a classifier are related to stratified sampling to re-balance the training data. This module will walk you through both stratified sampling methods and more novel approaches to model data sets with unbalanced classes.