R: Complete Machine Learning Solutions

R: Complete Machine Learning Solutions, available at $19.99, has an average rating of 3.05, with 125 lectures, 12 quizzes, based on 55 reviews, and has 583 subscribers.

You will learn about Create and inspect the transaction dataset and perform association analysis with the Apriori algorithm Predict possible churn users with the classification approach Implement the clustering method to segment customer data Compress images with the dimension reduction method Build a product recommendation system This course is ideal for individuals who are If you are interested in understanding machine learning concepts and building real-time projects with R, then this is the perfect course for you! It is particularly useful for If you are interested in understanding machine learning concepts and building real-time projects with R, then this is the perfect course for you! .

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Summary

Title: R: Complete Machine Learning Solutions

Price: $19.99

Average Rating: 3.05

Number of Lectures: 125

Number of Quizzes: 12

Number of Published Lectures: 125

Number of Published Quizzes: 12

Number of Curriculum Items: 137

Number of Published Curriculum Objects: 137

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Create and inspect the transaction dataset and perform association analysis with the Apriori algorithm
Predict possible churn users with the classification approach
Implement the clustering method to segment customer data
Compress images with the dimension reduction method
Build a product recommendation system

Who Should Attend

If you are interested in understanding machine learning concepts and building real-time projects with R, then this is the perfect course for you!

Target Audiences

If you are interested in understanding machine learning concepts and building real-time projects with R, then this is the perfect course for you!

Are you interested in understanding machine learning concepts and building real-time projects with R, but don’t know where to start? Then, this is the perfect course for you!

The aim of machine learning is to uncover hidden patterns, unknown correlations, and find useful information from data. In addition to this, through incorporation with data analysis, machine learning can be used to perform predictive analysis. With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data.

Machine learning has similarities to the human reasoning process. Unlike traditional analysis, the generated model cannot evolve as data is accumulated. Machine learning can learn from the data that is processed and analyzed. In other words, the more data that is processed, the more it can learn.

R, as a dialect of GNU-S, is a powerful statistical language that can be used to manipulate and analyze data. Additionally, R provides many machine learning packages and visualization functions, which enable users to analyze data on the fly. Most importantly, R is open source and free.

Using R greatly simplifies machine learning. All you need to know is how each algorithm can solve your problem, and then you can simply use a written package to quickly generate prediction models on data with a few command lines.

By taking this course, you will gain a detailed and practical knowledge of R and machine learning concepts to build complex machine learning models. �

What details do you cover in this course?

We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.

This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.

We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.

We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.

We will discover associated terms and underline frequent patterns from transaction data.

We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.

By the end of this course, we will build our own project in the e-commerce domain.�

This course will take you from the very basics of R to creating insightful machine learning models with R.

We have combined the best of the following Packt products:

R Machine Learning Solutions by Yu-Wei, Chiu (David Chiu)
Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
R Machine Learning By Example by Raghav Bali and Dipanjan Sarkar

Testimonials:

The source content have been received well by the audience. Here is a one of the reviews:

“good product, I enjoyed it“

– Ertugrul Bayindir

Meet your expert instructors:

Yu-Wei, Chiu (David Chiu) is the founder of LargitData a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems.�

Dipanjan Sarkaris an IT engineer at Intel, the world’s largest silicon company, where he works on analytics, business intelligence, and application development. His areas of specialization includes software engineering, data science, machine learning, and text analytics.

Raghav Balihas a master’s degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the world’s largest silicon company, where he works on analytics, business intelligence, and application development.�

Meet your managing editor:

This course has been planned and designed for you by me, Tanmayee Patil. I’m here to help you be successful every step of the way, and get maximum value out of your course purchase. If you have any questions along the way, you can reach out to me and our author group via the instructor contact feature on Udemy.

Course Curriculum

Chapter 1: Getting Started with R

Lecture 1: Introduction

Lecture 2: Downloading and Installing R

Lecture 3: Downloading and Installing RStudio

Lecture 4: Installing and Loading Packages

Lecture 5: Reading and Writing Data

Lecture 6: Using R to Manipulate Data

Lecture 7: Applying Basic Statistics

Lecture 8: Visualizing Data

Lecture 9: Getting a Dataset for Machine Learning

Chapter 2: Data Exploration with RMS Titanic

Lecture 1: Reading a Titanic Dataset from a CSV File

Lecture 2: Converting Types on Character Variables

Lecture 3: Detecting Missing Values

Lecture 4: Imputing Missing Values

Lecture 5: Exploring and Visualizing Datac

Lecture 6: Predicting Passenger Survival with a Decision Tree

Lecture 7: Validating the Power of Prediction with a Confusion Matrix

Lecture 8: Assessing Performance with the ROC Curve

Chapter 3: R and Statistics

Lecture 1: Understanding Data Sampling in R

Lecture 2: Operating a probability distribution in R

Lecture 3: Working with univariate descriptive statistics in R

Lecture 4: Performing Correlations and Multivariate Analysis

Lecture 5: Operating Linear Regression and Multivariate Analysis

Lecture 6: Conducting an Exact Binomial Test

Lecture 7: Performing Student's t-test

Lecture 8: Performing the Kolmogorov-Smirnov Test

Lecture 9: Understanding the Wilcoxon Rank Sum and Signed Rank Test

Lecture 10: Working with Pearson's Chi-Squared Test

Lecture 11: Conducting a One-Way ANOVA

Lecture 12: Performing a Two-Way ANOVA

Chapter 4: Understanding Regression Analysis

Lecture 1: Fitting a Linear Regression Model with lm

Lecture 2: Summarizing Linear Model Fits

Lecture 3: Using Linear Regression to Predict Unknown Values

Lecture 4: Generating a Diagnostic Plot of a Fitted Model

Lecture 5: Fitting a Polynomial Regression Model with lm

Lecture 6: Fitting a Robust Linear Regression Model with rlm

Lecture 7: Studying a case of linear regression on SLID data

Lecture 8: Applying the Gaussian Model for Generalized Linear Regression

Lecture 9: Applying the Poisson model for Generalized Linear Regression

Lecture 10: Applying the Binomial Model for Generalized Linear Regression

Lecture 11: Fitting a Generalized Additive Model to Data

Lecture 12: Visualizing a Generalized Additive Model

Lecture 13: Diagnosing a Generalized Additive Model

Chapter 5: Classification (I) – Tree, Lazy, and Probabilistic

Lecture 1: Preparing the Training and Testing Datasets

Lecture 2: Building a Classification Model with Recursive Partitioning Trees

Lecture 3: Visualizing a Recursive Partitioning Tree

Lecture 4: Measuring the Prediction Performance of a Recursive Partitioning Tree

Lecture 5: Pruning a Recursive Partitioning Tree

Lecture 6: Building a Classification Model with a Conditional Inference Tree

Lecture 7: Visualizing a Conditional Inference Tree

Lecture 8: Measuring the Prediction Performance of a Conditional Inference Tree

Lecture 9: Classifying Data with the K-Nearest Neighbor Classifier

Lecture 10: Classifying Data with Logistic Regression

Lecture 11: Classifying data with the Naïve Bayes Classifier

Chapter 6: Classification (II) – Neural Network and SVM

Lecture 1: Classifying Data with a Support Vector Machine

Lecture 2: Choosing the Cost of an SVM

Lecture 3: Visualizing an SVM Fit

Lecture 4: Predicting Labels Based on a Model Trained by an SVM

Lecture 5: Tuning an SVM

Lecture 6: Training a Neural Network with neuralnet

Lecture 7: Visualizing a Neural Network Trained by neuralnet

Lecture 8: Predicting Labels based on a Model Trained by neuralnet

Lecture 9: Training a Neural Network with nnet

Lecture 10: Predicting labels based on a model trained by nnet

Chapter 7: Model Evaluation

Lecture 1: Estimating Model Performance with k-fold Cross Validation

Lecture 2: Performing Cross Validation with the e1071 Package

Lecture 3: Performing Cross Validation with the caret Package

Lecture 4: Ranking the Variable Importance with the caret Package

Lecture 5: Ranking the Variable Importance with the rminer Package

Lecture 6: Finding Highly Correlated Features with the caret Package

Lecture 7: Selecting Features Using the caret Package

Lecture 8: Measuring the Performance of the Regression Model

Lecture 9: Measuring Prediction Performance with a Confusion Matrix

Lecture 10: Measuring Prediction Performance Using ROCR

Lecture 11: Comparing an ROC Curve Using the caret Package

Lecture 12: Measuring Performance Differences between Models with the caret Package

Chapter 8: Ensemble Learning

Lecture 1: Classifying Data with the Bagging Method

Lecture 2: Performing Cross Validation with the Bagging Method

Lecture 3: Classifying Data with the Boosting Method

Lecture 4: Performing Cross Validation with the Boosting Method

Lecture 5: Classifying Data with Gradient Boosting

Lecture 6: Calculating the Margins of a Classifier

Lecture 7: Calculating the Error Evolution of the Ensemble Method

Lecture 8: Classifying Data with Random Forest

Lecture 9: Estimating the Prediction Errors of Different Classifiers

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 5 votes
2 stars: 3 votes
3 stars: 10 votes
4 stars: 21 votes
5 stars: 16 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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