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Use over 100 solutions to analyze data and build predictive models.


Course Description

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 we 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.

Learning Outcomes

  • 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

Pre-requisite

No prior knowledge of R is required

Who is this course intended 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!

Your Instructor

Packt Publishing

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 everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Getting Started with R
Introduction 00:00:00
Downloading and Installing R 00:00:00
Downloading and Installing RStudio 00:00:00
Installing and Loading Packages 00:00:00
Reading and Writing Data 00:00:00
Using R to Manipulate Data 00:00:00
Applying Basic Statistics 00:00:00
Visualizing Data 00:00:00
Getting a Dataset for Machine Learning 00:00:00
Data Exploration with RMS Titanic
Reading a Titanic Dataset from a CSV File 00:00:00
Converting Types on Character Variables 00:00:00
Detecting Missing Values 00:00:00
Imputing Missing Values 00:00:00
Exploring and Visualizing Datac 00:00:00
Predicting Passenger Survival with a Decision Tree 00:00:00
Validating the Power of Prediction with a Confusion Matrix 00:00:00
Assessing Performance with the ROC Curve 00:00:00
R and Statistics
Understanding Data Sampling in R 00:00:00
Operating a probability distribution in R 00:00:00
Working with univariate descriptive statistics in R 00:00:00
Performing Correlations and Multivariate Analysis 00:00:00
Operating Linear Regression and Multivariate Analysis 00:00:00
Conducting an Exact Binomial Test 00:00:00
Performing Student’s t-test 00:00:00
Performing the Kolmogorov-Smirnov Test 00:00:00
Understanding the Wilcoxon Rank Sum and Signed Rank Test 00:00:00
Working with Pearson’s Chi-Squared Test 00:00:00
Conducting a One-Way ANOVA 00:00:00
Performing a Two-Way ANOVA 00:00:00
Understanding Regression Analysis
Fitting a Linear Regression Model with lm 00:00:00
Summarizing Linear Model Fits 00:00:00
Using Linear Regression to Predict Unknown Values 00:00:00
Generating a Diagnostic Plot of a Fitted Model 00:00:00
Fitting a Polynomial Regression Model with lm 00:00:00
Fitting a Robust Linear Regression Model with rlm 00:00:00
Studying a case of linear regression on SLID data 00:00:00
Applying the Gaussian Model for Generalized Linear Regression 00:00:00
Applying the Poisson model for Generalized Linear Regression 00:00:00
Applying the Binomial Model for Generalized Linear Regression 00:00:00
Fitting a Generalized Additive Model to Data 00:00:00
Visualizing a Generalized Additive Model 00:00:00
Diagnosing a Generalized Additive Model 00:00:00
Classification (I) – Tree, Lazy, and Probabilistic
Preparing the Training and Testing Datasets 00:00:00
Building a Classification Model with Recursive Partitioning Trees 00:00:00
Visualizing a Recursive Partitioning Tree 00:00:00
Measuring the Prediction Performance of a Recursive Partitioning Tree 00:00:00
Pruning a Recursive Partitioning Tree 00:00:00
Building a Classification Model with a Conditional Inference Tree 00:00:00
Visualizing a Conditional Inference Tree 00:00:00
Measuring the Prediction Performance of a Conditional Inference Tree 00:00:00
Classifying Data with the K-Nearest Neighbor Classifier 00:00:00
Classifying Data with Logistic Regression 00:00:00
Classifying data with the Naïve Bayes Classifier 00:00:00
Classification (II) – Neural Network and SVM
Classifying Data with a Support Vector Machine 00:00:00
Choosing the Cost of an SVM 00:00:00
Visualizing an SVM Fit 00:00:00
Predicting Labels Based on a Model Trained by an SVM 00:00:00
Tuning an SVM 00:00:00
Training a Neural Network with neuralnet 00:00:00
Visualizing a Neural Network Trained by neuralnet 00:00:00
Predicting Labels based on a Model Trained by neuralnet 00:00:00
Training a Neural Network with nnet 00:00:00
Predicting labels based on a model trained by nnet 00:00:00
Model Evaluation
Estimating Model Performance with k-fold Cross Validation 00:00:00
Performing Cross Validation with the e1071 Package 00:00:00
Performing Cross Validation with the caret Package 00:00:00
Ranking the Variable Importance with the caret Package 00:00:00
Ranking the Variable Importance with the rminer Package 00:00:00
Finding Highly Correlated Features with the caret Package 00:00:00
Selecting Features Using the caret Package 00:00:00
Measuring the Performance of the Regression Model 00:00:00
Measuring Prediction Performance with a Confusion Matrix 00:00:00
Measuring Prediction Performance Using ROCR 00:00:00
Comparing an ROC Curve Using the caret Package 00:00:00
Measuring Performance Differences between Models with the caret Package 00:00:00
Ensemble Learning
Classifying Data with the Bagging Method 00:00:00
Performing Cross Validation with the Bagging Method 00:00:00
Classifying Data with the Boosting Method 00:00:00
Performing Cross Validation with the Boosting Method 00:00:00
Classifying Data with Gradient Boosting 00:00:00
Calculating the Margins of a Classifier 00:00:00
Calculating the Error Evolution of the Ensemble Method 00:00:00
Classifying Data with Random Forest 00:00:00
Estimating the Prediction Errors of Different Classifiers 00:00:00
Clustering
Clustering Data with Hierarchical Clustering 00:00:00
Cutting Trees into Clusters 00:00:00
Clustering Data with the k-Means Method 00:00:00
Drawing a Bivariate Cluster Plot 00:00:00
Comparing Clustering Methods 00:00:00
Extracting Silhouette Information from Clustering 00:00:00
Obtaining the Optimum Number of Clusters for k-Means 00:00:00
Clustering Data with the Density-Based Method 00:00:00
Clustering Data with the Model-Based Method 00:00:00
Visualizing a Dissimilarity Matrix 00:00:00
Validating Clusters Externally 00:00:00
Association Analysis and Sequence Mining
Transforming Data into Transactions 00:00:00
Displaying Transactions and Associations 00:00:00
Mining Associations with the Apriori Rule 00:00:00
Pruning Redundant Rules 00:00:00
Visualizing Association Rules 00:00:00
Mining Frequent Itemsets with Eclat 00:00:00
Creating Transactions with Temporal Information 00:00:00
Mining Frequent Sequential Patterns with cSPADE 00:00:00
Dimension Reduction
Performing Feature Selection with FSelector 00:00:00
Performing Dimension Reduction with PCA 00:00:00
Determining the Number of Principal Components Using the Scree Test 00:00:00
Determining the Number of Principal Components Using the Kaiser Method 00:00:00
Visualizing Nultivariate Data Using biplot 00:00:00
Performing Dimension Reduction with MDS 00:00:00
Reducing Dimensions with SVD 00:00:00
Compressing Images with SVD 00:00:00
Performing Nonlinear Dimension Reduction with ISOMAP 00:00:00
Performing Nonlinear Dimension Reduction with Local Linear Embedding 00:00:00
Big Data Analysis with R and Hadoop
Preparing the RHadoop Environment 00:00:00
Installing rmr2 00:00:00
Installing rhdfs 00:00:00
Operating HDFS with rhdfs 00:00:00
Implementing a Word Count Problem with RHadoop 00:00:00
Comparing the Performance between an R MapReduce and a Standard R Program 00:00:00
Testing and Debugging the rmr2 Program 00:00:00
Installing plyrmr 00:00:00
Manipulating Data with plyrmr 00:00:00
Conducting Machine Learning with RHadoop 00:00:00
Configuring RHadoop Clusters on Amazon EMR 00:00:00

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