Learning Path: R: Complete Machine Learning & Deep Learning

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