We use cookies and similar technology on this website, which helps us to know a little bit about you and how you use our website. This improves the browsing experience for you and enables us to tailor better products and services to you and others. Cookies are stored locally on your computer or mobile device.
To accept cookies continue browsing as normal or go to the Cookies Notice for more information and to set your preferences.
Applied Machine Learning With R
Machine learning is changing the manner in which we use data and training system. It has gradually spread it's span through our gadgets, from self-driving cars to the computerized chatbots. To characterize machine learning in the most straightforward terms, it is essentially the capacity to prepare computers to think for themselves in light of the situations that occurs.
With machine learning now being part of all major businesses it is important for developers to get a hang of this amazing technology. Machine learning is a complex concept that uses algorithms to configure coding that will enable the computers to learn from lot of data. We have created a simple and easy course to help you learn ML and AI using R.
Our course on Machine Learning with R helps you understand the core concepts of machine learning followed by different machine learning algorithms and implementing those machine learning algorithms with R. At the end of this practical and hands-on course, you will have all that you need to really begin using machine learning algorithms and you will learn to add them in your own projects.
Lets have a look at the list of modules you will find in this course:-
So what are you waiting for? Grab the opportunity by enrolling your name and we will see you inside!
1 | Introduction | ||
2 | Starting up- Machine learning with R | ||
3 | What is Artificial Intelligence and machine learning | ||
4 | Flow of machine learning | ||
5 | Machine Learning vs Deep Learning |
6 | R tool and installation | ||
7 | R data structures |
8 | Basics of Machine learning | ||
9 | Supervised and unsupervised learning | ||
10 | Case study- K means clustering | ||
11 | Installation of H2O package | ||
12 | Performing Regression with H2O | ||
13 | Analysing the regression with H2O |