Advanced Machine Learning with R [Video]
Learn advanced techniques like hyperparameter tuning, deep learning in a step by step manner with examples. Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune hyper-parameters using a dataset of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data. Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further. Style and Approach In a step-by-step manner, these videos will cover more advanced topics in Machine learning. A variety of practical, solid, real-world problem types will be used to illustrate these concepts.
What you will learn
Work with advanced techniques in machine learning with R Explore advanced techniques such as hyperparameter tuning and deep learning Work with Neural Networks (NNs) and explore, implement, and classify documents Get to know hyper-parameter tuning by exploring and iterating through parameters Understand unsupervised learning, clustering data, and visualizing Know how to evaluate the performance of your models and put your model into use Work with a variety of real-world algorithms that suit your problem