Advanced Machine Learning with R
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Advanced Machine Learning with R

Learn advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1 student enrolled
Created by Packt Publishing
Last updated 9/2017
English [Auto-generated]
Current price: $10 Original price: $125 Discount: 92% off
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  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Work with advanced techniques in machine learning with R
  • Explore advanced techniques such as hyper parameter 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
View Curriculum
  • If you are an aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, this video is for you

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 data set 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.

About the Author

Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group.
In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.

Who is the target audience?
  • Readers are not expected to have any knowledge of the development of Artificial Intelligence or Machine Learning systems. If you want to understand how the R programming environment and packages can be used to develop machine learning systems, then this is the perfect course for you.
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Curriculum For This Course
19 Lectures
Sonar Data – Hyper-Parameter Tuning
5 Lectures 22:01

This video gives an overview of entire course.

Preview 01:58

The goal of this video is to examine the data we will use.

Explore Sonar Data Set

The aim of this video is to do exploratory analysis of the vote92 data set.

Tuning Grids

The aim of this video is to improve our model.
Iterating – Improving our Tuning

The goal of this video is to examine the test set predictions vs actuals.

Final Results
Neural Network
5 Lectures 22:00

In this video, we will see what foundations of the math behind Neural Networks are.

Preview 07:34

The goal of this video is to examine the correlations and types of DNA data that we will use for our examples.

Explore the DNA Set

This video will show how to use caret to build a model that classifies DNA data.

Implement a Neural Network

Try another neural network algorithm

Multi-layer Perceptron

Sometimes, you’ll want to call the modelling algorithm directly without the caret wrapper. In this video, we will work with a neural network algorithm directly.

One Hot Encoding and MLP
Keras – Deep Learning on the GPU
5 Lectures 31:01

In this video, we go over the background of the Keras package and how it works.

Preview 04:00

In this video, we setup our environment for running Keras.
Installing Keras

The goal of this video is to use Keras to classify the DNA data from last section.

Neural Network in Keras

The aim of this video is to explore the CIFAR10 image data set.

CIFAR10 Data Set

This video explains CNN. Convolutional Neural Networks (CNNs) are a powerful technique for image classification.

Convolutional Neural Network
Deploying Your Model
4 Lectures 16:49
After we train our model, we want to save it so it can be run on new data later. This will be explained in this video.
Preview 03:23

This video will resolve this question – what if we want to implement the prediction function in another language.

Saving Your Model for Another Language

The aim of this video is to introduce to the shiny package, one option for presenting modelling data in a production system.

Shiny Web Interfaces

In this video, we will see more complex shiny example, where we input features and predict an outcome.

Wrapping Your Model in Shiny
About the Instructor
Packt Publishing
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