This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
By the end of this course you should be able to:
Differentiate uses and applications of classification and regression in the context of supervised machine learning
Describe and use linear regression models
Use a variety of error metrics to compare and select a linear regression model that best suits your data
Articulate why regularization may help prevent overfitting
Use regularization regressions: Ridge, LASSO, and Elastic net
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through t…
There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will all…
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
In this section, you will understand the relationship between the loss function and the different regularization types.
In this assignment, you will apply regression techniques to analyze a dataset of your choice. Your task is to preprocess the data, build and compare models, extract insights, and suggest next steps.You will focus on presenting key findings and insights—not the code. You may include visuals to suppo…
Some points apply to every course of this kind; see how we rank.
IBM is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (84/100).
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
An introduction to cybersecurity for technical and non-technical audiences alike. Learn how to secure your accounts, data, systems, and software against today’s threats and how to recognize and evaluate tomorrow’s as well, both at home and at work. Learn how to preserve your own privacy. Learn to v…
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
