**Applied Machine Learning With R**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5h 01m | 757 MB

Learn machine learning and implement practical algorithms using R programming

Machine learning is here and it is changing the way businesses work! From the Netflix recommendation engine to Google’s self-driving car, it’s all machine learning. Machine learning explores the development and use of algorithms that can gain from data. ML Algorithms provide the ability to learn at an accelerated pace as more and more datasets are available for training. It is very similar to how the human mind learns. In this course, you will also learn about machine learning and deep learning and will see how R can be used as a tool (to show output) and also in your ML projects. The course also covers packages that implement machine learning with TensorFlow and H2O. TensorFlow is a Python package that is implemented in R as well. The course also covers artificial neural networks. Here you get to learn how to create our own neural networks and implement them in R. Last but not least, the sixth module is Decision Tree and Text mining, a well know pattern involved in data science, again a new concept in machine learning. All the modules throw light on how machine learning implementation is easy and simple using R. So what are you waiting for? Begin your epic journey to being an awesome ML programmer with this applied R course.

Our course, Applied Machine Learning with R, uses R, the powerful data manipulation language, to solve ML problems. This unique course will help you get started on your journey to becoming an AI and machine learning developer.

What You Will Learn

- Learn to implement ML algorithms in R
- Learn deep learning in R
- Learn to build neural networks in R
- Learn to work with decision trees.

01 Introduction

02 Starting up- Machine learning with R

03 What is Artificial Intelligence and machine learning

04 Flow of machine learning

05 Machine Learning vs Deep Learning

06 R tool and installation

07 R data structures

08 Basics of Machine learning

09 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

14 Tensorflow package

15 Performing Regression with TensorFlow

16 Analysing the regression with TensorFlow

17 Performance of model using TensorFlow

18 Caret Package for Machine Learning

19 Machine Learning with dataset

20 Iris dataset Implementation

21 Evaluation of Algorithms with models

22 Selecting Best Model in Machine Learning

23 Creating and Visualizing Neural networks

24 Demonstration of sample neural network

25 Prediction Analysis of neural network

26 Cross Validation Box plot

27 Activity- Dataset to Neural Network

28 Cluster Generation

29 Cluster Generation Output Analysis

30 Decision Trees of Machine Learning

31 Car Evaluation Problem Statement

32 Plotting a Decision Tree

33 Prediction Analysis- Decision Tree

34 Introduction to Text Mining

35 Text Mining with R