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Applied Machine Learning With R

Video Description

Learn machine learning and implement practical algorithms using R programming

About This Video

  • Basic knowledge of R is required to complete this course

In Detail

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.

All the code and supporting files for this course are available at: https://github.com/PacktPublishing/Applied-Machine-Learning-With-R

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Introduction
    1. Introduction 00:01:42
    2. Starting up- Machine learning with R 00:08:03
    3. What is Artificial Intelligence and machine learning? 00:04:32
    4. Flow of machine learning 00:05:04
    5. Machine Learning vs Deep Learning 00:05:15
  2. Chapter 2 : R programming tool
    1. R tool and installation 00:05:08
    2. R data structures 00:10:55
  3. Chapter 3 : H2O Package
    1. Basics of Machine learning 00:04:33
    2. Supervised and unsupervised learning 00:10:54
    3. Case study- K means clustering 00:06:34
    4. Installation of H2O package 00:05:58
    5. Performing Regression with H2O 00:14:56
    6. Analysing the regression with H2O 00:11:10
  4. Chapter 4 : TensorFlow Package
    1. Tensorflow package 00:05:15
    2. Performing Regression with TensorFlow 00:09:38
    3. Analysing the regression with TensorFlow 00:14:16
    4. Performance of model using TensorFlow 00:09:56
  5. Chapter 5 : First Machine Learning
    1. Caret Package for Machine Learning 00:14:06
    2. Machine Learning with dataset 00:11:54
    3. Iris dataset Implementation 00:07:17
    4. Evaluation of Algorithms with models 00:08:55
    5. Selecting Best Model in Machine Learning 00:06:18
  6. Chapter 6 : Artificial Neural Networks
    1. Creating and Visualizing Neural networks 00:05:32
    2. Demonstration of sample neural network 00:12:45
    3. Prediction Analysis of neural network 00:10:46
    4. Cross Validation Box plot 00:10:16
    5. Activity- Dataset to Neural Network 00:10:05
  7. Chapter 7 : Cluster Generation
    1. Cluster Generation 00:06:30
    2. Cluster Generation Output Analysis 00:08:32
  8. Chapter 8 : Decision Trees
    1. Decision Trees of Machine Learning 00:05:18
    2. Car Evaluation Problem Statement 00:12:52
    3. Plotting a Decision Tree 00:10:45
    4. Prediction Analysis- Decision Tree 00:06:35
  9. Chapter 9 : Text Mining
    1. Introduction to Text Mining 00:09:38
    2. Text Mining with R 00:09:55