Course Description

Would you like to master machine learning with R? This course will introduce its fundamental principles and how to use its capabilities to make predictions and business decisions based on the training dataset. Investigate the wide variety of applications using machine learning instead of conventional algorithms to perform the desired tasks. This course will clarify how it helps solve troublesome problems too complicated to write a program for. So, instead of writing these programs by hand, learn to collect illustrations that specify the correct out for a given input. The machine learning algorithm will use these examples and produce a program that will do that job.

We’ll start by introducing you to the requirements for machine learning to work. The essential prerequisite is that there should be a pattern between the input and the output of the data, and you can only discover this pattern if there is a sizeable amount of data. Next, you will examine different approaches and the different types of algorithms under each category. Finally, understand how anomaly detection works, which has to do with training the system using normal instances to determine if a new instance is standard when it encounters it. This capability is helpful in fraud detection and in catching manufacturing defects. What is the ultimate objective of machine learning? The general aim is to determine a model’s ability to perform well on new data.

Discover that the first algorithm taught in machine learning is generally linear regression and why linear regression serves as an excellent introduction to how it works. Data analysis is a critical skill to have as a data scientist because it enables you to develop a better understanding of your data and helps you reach valuable conclusions. This skill is taught next and is a crucial step to take before applying any machine learning. It lets you know what you are dealing with, what algorithms to use, and what features to use to train the model. Finally, you will study data manipulation and visualization, managing outliers, linear regression in R, and logistic regression in R. This course will interest students and working professionals passionate about data science and machine learning. Why delay! Sign up today. This course has not been updated with the use of Generative AI models, like ChatGPT.

What you'll learn in this course?

• Technology

• Programming

• Data Analysis

• Data Science

• Machine Learning

• R Programming