Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Students will learn to train a model, evaluate its performance, and improve its performance.
Collect, explore, visualize, and prepare data for machine learning problems using R
Understand how machine learning algorithms make predictions
Identify appropriate machine learning algorithms for your project
Train, evaluate, monitor, and improve machine learning models
Implement machine learning solutions
About this course:
This course introduces machine learning using R. Students will learn structured and unstructured data processing, linear regression modeling and non-linear modeling methods used in machine learning algorithm development, optimization techniques, neural networks, and deep learning. This field is made possible due to the rapid and simultaneous evolution of available data, statistical methods, and computing power. Students learn the origins and practical applications of machine learning, how knowledge is defined and represented by computers, and the basic concepts that differentiate machine learning approaches. Machine learning algorithms can be divided into two main groups: supervised learners who are used to construct predictive models and unsupervised learners who are used to build descriptive models. Students learn the classification, numeric predictor, pattern detection, and clustering algorithms. Students learn to train a model, evaluate its performance, and improve its performance. Algorithm uses are illustrated with real-world cases, such as breast cancer diagnosis, spam filtering, identifying bank loan risk, predicting medical expenses, estimating wine quality, identifying groceries frequently purchased together, and finding teen market segments. A foundational understanding of coding, particularly in R, is necessary for success in this course.
Prerequisites
Before embarking on the Machine Learning course, it is imperative to establish a robust foundational knowledge. We suggest the following preparatory steps: Introduction to Data Science to acquaint yourself with the basic principles of data science.
Statistics Background: In case you lack proficiency in statistics, we recommend enrolling in a course such as Introduction to Statistical Reasoning. Grasping statistical concepts is a key determinant of success in machine learning.
Foundational understanding of coding, particularly in R, is necessary for success in this course.
We use cookies to understand how you use our site and to improve your experience, including personalizing content and to store your content preferences. By continuing to use our site, you accept our use of cookies.
Read our privacy policy.
