Machine Learning using Advanced Algorithms and Visualization
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Machine Learning using Advanced Algorithms and Visualization

Explore advanced algorithm concepts such as random forest vector machine, K- nearest, & more through real-world 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.
10 students enrolled
Created by Packt Publishing
Last updated 6/2017
English
Current price: $10 Original price: $125 Discount: 92% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 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 algorithms and techniques to enable efficient machine learning using the R programming language
  • Explore concepts such as the random forest algorithm
  • Work with support vector machine and examine and plot the results
  • Find out how to use the K-Nearest Neighbor for data projection
  • Work with a variety of real-world algorithms that suit your problem
View Curriculum
Requirements
  • If you are an aspiring data scientist who is familiar with basic of R language, data frames, and some basic knowledge in statistics. Readers are not expected to have any knowledge of the development of Artificial Intelligence or Machine Learning systems.
Description

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.

In this course, you will work through various examples on advanced algorithms, and focus a bit more on some visualization options. We’ll start by showing you how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. Then, we’ll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix.  After that, you will look into the next example on soil classification from satellite data using K-Nearest Neighbor where you will predict what neighborhood a house is in based on other data about it. Finally, you’ll dive into the last example of predicting a movie genre based on its title, where you will use the tm package and learn some techniques for working with text data.

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 U.S. Midwest. He also used to work in web application development and mobile development.He is the organizer of the Cleveland R User Group.

In his job, he uses deep neural networks to help automate of lot of conversation classification problems. In addition, he works on side projects such as researching Artificial Intelligence and Machine Learning. Personally.

Outside of Data Science, he is interested in mathematical computation in general. He is a lifelong learner of math and really enjoys applying wherever he can. Recently, he has spent some time in financial analysis and game development. He also knows a variety of languages, such as R, Python, Ruby, PHP, C/C++, and more.

Who is the target audience?
  • If you are looking to understand how the R programming environment and packages can be used for developing machine learning systems, then this is the perfect course for you.
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Curriculum For This Course
19 Lectures
01:15:17
+
Random Forest
6 Lectures 24:38

This video provides an overview of the entire course.

Preview 01:42

The goal of this video is to explain the random forest algorithm.

Random Forest Overview
07:22

In this video, we will do exploratory analysis of the vote92 data set.

Exploring the Vote92 Data Set
06:57

In this video, we will use a randomForest() model.

Using a Random Forest Model
02:59

In this video, we will explore the model results closer.

Examining the model
03:16

In this video, we will examine the test set predictions versus actuals in terms of election results.

New Model and Final Results
02:22
+
Support Vector Machines
4 Lectures 12:01

The goal of this video is to understand the Support Vector Machine and EDA

Preview 05:08

In this video, we will create a support vector machine model.

Building an SVM Model
02:41

In this video, we will examine the results and create a support vector machine model with advanced parameters.

Examining the Results and Model
01:55

In this video, we will do a more advanced plot of the confusion matrix.

Visualizing a Confusion Matrix
02:17
+
K-Nearest Neighbor
4 Lectures 13:43

The goal of this video is to examine the Satellite data.

Preview 04:39

In this video, we will explore k-nearest in R.

Overview of K-Nearest Neighbor
04:58

In this video, we will create a k-nearest neighbor model.

Using KNN
01:35

In this video, we will do a more advanced plot of the records.

Visualizing KNN Results
02:31
+
Movie Reviews - Working With Text
5 Lectures 24:55

The goal of this video is to examine the movie data.

Preview 02:42

The goal of this video is to understand documents represented by vectors, such as count vectors.

Overview of Document Vectors
06:55

In this video, we will classify the documents we vectorized.

Classifying Document Matrices
07:30

In this video, we will cluster our documents and compare the clusters with the classification.

Clustering Documents
02:38

In this video, we will rank similar documents as you would in a search.

Similar Documents
05:10
About the Instructor
Packt Publishing
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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.