Practical Machine Learning with R

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.

Overview

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way.

Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the course, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them.

By the end of this course, you will have gained expertise in solving 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 it.

After completing this course, you will be able to:

• Define a problem that can be solved by training a machine learning model
• Obtain, verify and clean data before transforming it into the correct format for use
• Perform exploratory analysis and extract features from data
• Build models for regression, classification and clustering
• Evaluate the performance of a model with the right metrics
• Solve a classification problem using the neuralnet package
• Implement a decision tree using the random forest library

Course Length

2 days

Scope

Practical Machine Learning with R uses a practical and hands-on approach to teach all concepts. You will explore different machine learning algorithms with a project-based approach. By solving problems using concepts taught in the previous chapters, the course demystifies the complexity of machine learning and gives you the confidence to tackle even more challenging problems.

Target Audience

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.

Technical Requirements

Hardware:

For the optimal student experience, we recommend the following hardware configuration:

• Processor: Intel Core i5 or equivalent
• Memory: 4GB RAM (8 GB Preferred)
• Storage: 16 GB available space

Software:

We also recommend that you have the following software installed in advance:

• OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
• Browser
• R Studio
• R version 3.6 or later
• R libraries as needed (mice, caret, rpart, groupdata2, cvms, neuralnet, NeuralNetTools, rPref, mlbench, knitr, interplot, doParallel, car, and so on)

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