Unleash the true potential of R to unlock the hidden layers of data
What you'll learn
Develop R packages and extend the functionality of your model
Perform pre-model building steps
Understand the working behind core machine learning algorithms
Build recommendation engines using multiple algorithms
Incorporate R and Hadoop to solve machine learning problems on Big Data
Understand advanced strategies that help speed up your R code
Learn the basics of deep learning and artificial neural networks
Learn the intermediate and advanced concepts of artificial and recurrent neural networks
Requirements
Basic knowledge of R would be beneficial
Knowledge of linear algebra and statistics is required
Description
Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you.
Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios.
The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering.
By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects.
Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth
About the Authors
Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies.
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. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process Big Data and apply data mining techniques for data analysis.
Vincenzo Lomonaco is a deep learning PhD student at the University of Bologna and founder of ContinuousAI, an open source project aiming to connect people and reorganize resources in the context of continuous learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses machine learning and computer architectures in the same department.
Overview
Section 1: Mastering R Programming
Lecture 1 The Course Overview
Lecture 2 Performing Univariate Analysis
Lecture 3 Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
Lecture 4 Detecting and Treating Outlier
Lecture 5 Treating Missing Values with `mice`
Lecture 6 Building Linear Regressors
Lecture 7 Interpreting Regression Results and Interactions Terms
Lecture 8 Performing Residual Analysis & Extracting Extreme Observations Cook's Distance
Lecture 9 Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
Lecture 10 Validating Model Performance on New Data with k-Fold Cross Validation
Lecture 11 Building Non-Linear Regressors with Splines and GAMs
Lecture 12 Building Logistic Regressors, Evaluation Metrics, and ROC Curve
Lecture 13 Understanding the Concept and Building Naive Bayes Classifier
Lecture 14 Building k-Nearest Neighbors Classifier
Lecture 15 Building Tree Based Models Using RPart, cTree, and C5.0
Lecture 16 Building Predictive Models with the caret Package
Lecture 17 Selecting Important Features with RFE, varImp, and Boruta
Lecture 18 Building Classifiers with Support Vector Machines
Lecture 19 Understanding Bagging and Building Random Forest Classifier
Lecture 20 Implementing Stochastic Gradient Boosting with GBM
Lecture 21 Regularization with Ridge, Lasso, and Elasticnet
Lecture 22 Building Classifiers and Regressors with XGBoost
Lecture 23 Dimensionality Reduction with Principal Component Analysis
Lecture 24 Clustering with k-means and Principal Components
Lecture 25 Determining Optimum Number of Clusters
Lecture 26 Understanding and Implementing Hierarchical Clustering
Lecture 27 Clustering with Affinity Propagation
Lecture 28 Building Recommendation Engines
Lecture 29 Understanding the Components of a Time Series, and the xts Package
Lecture 30 Stationarity, De-Trend, and De-Seasonalize
Lecture 31 Understanding the Significance of Lags, ACF, PACF, and CCF
Lecture 32 Forecasting with Moving Average and Exponential Smoothing
Lecture 33 Forecasting with Double Exponential and Holt Winters
Lecture 34 Forecasting with ARIMA Modelling
Lecture 35 Scraping Web Pages and Processing Texts
Lecture 36 Corpus, TDM, TF-IDF, and Word Cloud
Lecture 37 Cosine Similarity and Latent Semantic Analysis
Lecture 38 Extracting Topics with Latent Dirichlet Allocation
Lecture 39 Sentiment Scoring with tidytext and Syuzhet
Lecture 40 Classifying Texts with RTextTools
Lecture 41 Building a Basic ggplot2 and Customizing the Aesthetics and Themes
Lecture 42 Manipulating Legend, AddingText, and Annotation
Lecture 43 Drawing Multiple Plots with Faceting and Changing Layouts
Lecture 44 Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
Lecture 45 ggplot2 Extensions and ggplotly
Lecture 46 Implementing Best Practices to Speed Up R Code
Lecture 47 Implementing Parallel Computing with doParallel and foreach
Lecture 48 Writing Readable and Fast R Code with Pipes and DPlyR
Lecture 49 Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
Lecture 50 Interface C++ in R with RCpp
Lecture 51 Understanding the Structure of an R Package
Lecture 52 Build, Document, and Host an R Package on GitHub
Lecture 53 Performing Important Checks Before Submitting to CRAN
Lecture 54 Submitting an R Package to CRAN
Section 2: R Machine Learning solutions
Lecture 55 The Course Overview
Lecture 56 Downloading and Installing R
Lecture 57 Downloading and Installing RStudio
Lecture 58 Installing and Loading Packages
Lecture 59 Reading and Writing Data
Lecture 60 Using R to Manipulate Data
Lecture 61 Applying Basic Statistics
Lecture 62 Visualizing Data
Lecture 63 Getting a Dataset for Machine Learning
Lecture 64 Reading a Titanic Dataset from a CSV File
Lecture 65 Converting Types on Character Variables
Lecture 66 Detecting Missing Values
Lecture 67 Imputing Missing Values
Lecture 68 Exploring and Visualizing Data
Lecture 69 Predicting Passenger Survival with a Decision Tree
Lecture 70 Validating the Power of Prediction with a Confusion Matrix
Lecture 71 Assessing performance with the ROC curve
Lecture 72 Understanding Data Sampling in R
Lecture 73 Operating a Probability Distribution in R
Lecture 74 Working with Univariate Descriptive Statistics in R
Lecture 75 Performing Correlations and Multivariate Analysis
Lecture 76 Operating Linear Regression and Multivariate Analysis
Lecture 77 Conducting an Exact Binomial Test
Lecture 78 Performing Student's t-test
Lecture 79 Performing the Kolmogorov-Smirnov Test
Lecture 80 Understanding the Wilcoxon Rank Sum and Signed Rank Test
Lecture 81 Working with Pearson's Chi-Squared Test
Lecture 82 Conducting a One-Way ANOVA
Lecture 83 Performing a Two-Way ANOVA
Lecture 84 Fitting a Linear Regression Model with lm
Lecture 85 Summarizing Linear Model Fits
Lecture 86 Using Linear Regression to Predict Unknown Values
Lecture 87 Generating a Diagnostic Plot of a Fitted Model
Lecture 88 Fitting a Polynomial Regression Model with lm
Lecture 89 Fitting a Robust Linear Regression Model with rlm
Lecture 90 Studying a case of linear regression on SLID data
Lecture 91 Reducing Dimensions with SVD
Lecture 92 Applying the Poisson model for Generalized Linear Regression
Lecture 93 Applying the Binomial Model for Generalized Linear Regression
Lecture 94 Fitting a Generalized Additive Model to Data
Lecture 95 Visualizing a Generalized Additive Model
Lecture 96 Diagnosing a Generalized Additive Model
Lecture 97 Preparing the Training and Testing Datasets
Lecture 98 Building a Classification Model with Recursive Partitioning Trees
Lecture 99 Visualizing a Recursive Partitioning Tree
Lecture 100 Measuring the Prediction Performance of a Recursive Partitioning Tree
Lecture 101 Pruning a Recursive Partitioning Tree
Lecture 102 Building a Classification Model with a Conditional Inference Tree
Lecture 103 Visualizing a Conditional Inference Tree
Lecture 104 Measuring the Prediction Performance of a Conditional Inference Tree
Lecture 105 Classifying Data with the K-Nearest Neighbor Classifier
Lecture 106 Classifying Data with Logistic Regression
Lecture 107 Classifying data with the Naïve Bayes Classifier
Lecture 108 Classifying Data with a Support Vector Machine
Lecture 109 Choosing the Cost of an SVM
Lecture 110 Visualizing an SVM Fit
Lecture 111 Predicting Labels Based on a Model Trained by an SVM
Lecture 112 Tuning an SVM
Lecture 113 Training a Neural Network with neuralnet
Lecture 114 Visualizing a Neural Network Trained by neuralnet
Lecture 115 Predicting Labels based on a Model Trained by neuralnet
Lecture 116 Training a Neural Network with nnet
Lecture 117 Predicting labels based on a model trained by nnet
Lecture 118 Estimating Model Performance with k-fold Cross Validation
Lecture 119 Performing Cross Validation with the e1071 Package
Lecture 120 Performing Cross Validation with the caret Package
Lecture 121 Ranking the Variable Importance with the caret Package
Lecture 122 Ranking the Variable Importance with the rminer Package
Lecture 123 Finding Highly Correlated Features with the caret Package
Lecture 124 Selecting Features Using the Caret Package
Lecture 125 Measuring the Performance of the Regression Model
Lecture 126 Measuring Prediction Performance with a Confusion Matrix
Lecture 127 Measuring Prediction Performance Using ROCR
Lecture 128 Comparing an ROC Curve Using the Caret Package
Lecture 129 Measuring Performance Differences between Models with the caret Package
Lecture 130 Classifying Data with the Bagging Method
Lecture 131 Performing Cross Validation with the Bagging Method
Lecture 132 Classifying Data with the Boosting Method
Lecture 133 Performing Cross Validation with the Boosting Method
Lecture 134 Classifying Data with Gradient Boosting
Lecture 135 Calculating the Margins of a Classifier
Lecture 136 Calculating the Error Evolution of the Ensemble Method
Lecture 137 Classifying Data with Random Forest
Lecture 138 Estimating the Prediction Errors of Different Classifiers
Lecture 139 Clustering Data with Hierarchical Clustering
Lecture 140 Cutting Trees into Clusters
Lecture 141 Clustering Data with the k-Means Method
Lecture 142 Drawing a Bivariate Cluster Plot
Lecture 143 Comparing Clustering Methods
Lecture 144 Extracting Silhouette Information from Clustering
Lecture 145 Obtaining the Optimum Number of Clusters for k-Means
Lecture 146 Clustering Data with the Density-Based Method
Lecture 147 Clustering Data with the Model-Based Method
Lecture 148 Visualizing a Dissimilarity Matrix
Lecture 149 Validating Clusters Externally
Lecture 150 Transforming Data into Transactions
Lecture 151 Displaying Transactions and Associations
Lecture 152 Mining Associations with the Apriori Rule
Lecture 153 Pruning Redundant Rules
Lecture 154 Visualizing Association Rules
Lecture 155 Mining Frequent Itemsets with Eclat
Lecture 156 Creating Transactions with Temporal Information
Lecture 157 Mining Frequent Sequential Patterns with cSPADE
Lecture 158 Performing Feature Selection with FSelector
Lecture 159 Performing Dimension Reduction with PCA
Lecture 160 Determining the Number of Principal Components Using the Scree Test
Lecture 161 Determining the Number of Principal Components Using the Kaiser Method
Lecture 162 Visualizing Multivariate Data Using biplot
Lecture 163 Performing Dimension Reduction with MDS
Lecture 164 Reducing Dimensions with SVD
Lecture 165 Compressing Images with SVD
Lecture 166 Performing Nonlinear Dimension Reduction with ISOMAP
Lecture 167 Performing Nonlinear Dimension Reduction with Local Linear Embedding
Lecture 168 Preparing the RHadoop Environment
Lecture 169 Installing rmr2
Lecture 170 Installing rhdfs
Lecture 171 Operating HDFS with rhdfs
Lecture 172 Implementing a Word Count Problem with RHadoop
Lecture 173 Comparing the Performance between an R MapReduce Program & a Standard R Program
Lecture 174 Testing and Debugging the rmr2 Program
Lecture 175 Installing plyrmr
Lecture 176 Manipulating Data with plyrmr
Lecture 177 Conducting Machine Learning with RHadoop
Lecture 178 Configuring RHadoop Clusters on Amazon EMR
Section 3: Deep Learning with R
Lecture 179 The Course Overview
Lecture 180 Fundamental Concepts in Deep Learning
Lecture 181 Introduction to Artificial Neural Networks
Lecture 182 Classification with Two-Layers Artificial Neural Networks
Lecture 183 Probabilistic Predictions with Two-Layer ANNs
Lecture 184 Introduction to Multi-hidden-layer Architectures
Lecture 185 Tuning ANNs Hyper-Parameters and Best Practices
Lecture 186 Neural Network Architectures
Lecture 187 Neural Network Architectures Continued
Lecture 188 The LearningProcess
Lecture 189 Optimization Algorithms and Stochastic Gradient Descent
Lecture 190 Backpropagation
Lecture 191 Hyper-Parameters Optimization
Lecture 192 Introduction to Convolutional Neural Networks
Lecture 193 Introduction to Convolutional Neural Networks Continued
Lecture 194 CNNs in R
Lecture 195 Classifying Real-World Images with Pre-Trained Models
Lecture 196 Introduction to Recurrent Neural Networks
Lecture 197 Introduction to Long Short-Term Memory
Lecture 198 RNNs in R
Lecture 199 Use-Case – Learning How to Spell English Words from Scratch
Lecture 200 Introduction to Unsupervised and Reinforcement Learning
Lecture 201 Autoencoders
Lecture 202 Restricted Boltzmann Machines and Deep Belief Networks
Lecture 203 Reinforcement Learning with ANNs
Lecture 204 Use-Case – Anomaly Detection through Denoising Autoencoders
Lecture 205 Deep Learning for Computer Vision
Lecture 206 Deep Learning for Natural Language Processing
Lecture 207 Deep Learning for Audio Signal Processing
Lecture 208 Deep Learning for Complex Multimodal Tasks
Lecture 209 Other Important Applications of Deep Learning
Lecture 210 Debugging Deep Learning Systems
Lecture 211 GPU and MGPU Computing for Deep Learning
Lecture 212 A Complete Comparison of Every DL Packages in R
Lecture 213 Research Directions and Open Questions
The Learning Path is for machine learning engineers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R
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