Perform exploratory analysis and extract features from data.Obtain, verify and clean data before transforming it into the correct format for use.Define a problem that can be solved by training a machine learning model.In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them.īy the end of this course, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not over-train it.Īfter completing this course, you will be able to: As you progress through the course, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. ![]() Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. ![]() ![]() With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. Practical Machine Learning with R gives you the complete knowledge to solve your business problems - starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not over-train the model.
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