HARTMANN software group

Practical Machine Learning with R Training in San Bernardino

Enroll in or hire us to teach our Practical Machine Learning with R class in San Bernardino, California by calling us @303.377.6176. Like all HSG classes, Practical Machine Learning with R may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Practical Machine Learning with R may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  

Practical Machine Learning with R gives you the complete knowledge to solve your business problems - starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not over-train the model.

Course Length: 2 Days
Course Tuition: $890 (US)

Prerequisites
If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this course is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this course very useful. The course will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

Course Outline

  
Lesson 1: An Introduction to Machine Learning
 
The Machine Learning Process
Introduction to R
Machine Learning Models
Regression
Lesson 2: Data Cleaning and Pre-processing
 
Advanced Operations on Data Frames
Identifying the Input and Output Variables
Identifying the Category of Prediction
Handling Missing Values, Duplicates, and Outliers
Handling Outliers
Lesson 3: Feature Engineering
 
Types of Features
Time Series Features
Handling Categorical Variables
Derived Features or Domain-Specific Features
Adding Features to a Data Frame
Handling Redundant Features
Feature Selection
Lesson 4: Introduction to neuralnet and Evaluation Methods
 
Classification
Model Selection
Multiclass Classification Overview
Lesson 5: Linear and Logistic Regression Models
 
Regression
Linear Regression
Logistic Regression
Regression and Classification with Decision Trees
Model Selection by Multiple Disagreeing Metrics
Lesson 6: Unsupervised Learning
 
Overview of Unsupervised Learning (Clustering)
DIANA
Applications of Clustering
k-means Clustering

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