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.
Table of Contents
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
Resolve the captcha to access the links!