Last updated 6/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.50 GB | Duration: 17h 36m
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 ees using multiple algorithms Incorporate R and Hadoop to solve machine learning problems on Big Data Understand advanced strats 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 bs 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 eeering. 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 LaData, a startup company that mainly focuses on providing Big Data and machine learning products. He has previously worked for Trend Micro as a software eeer, 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 Eeering (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 Perfog 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 Perfog 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 Deteing Optimum Number of Clusters Lecture 26 Understanding and Implementing Hierarchical Clustering Lecture 27 Clustering with Affinity Propagation Lecture 28 Building Recommendation Ees Lecture 29 Understanding the Components of a 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 Sennt 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 Chag Layouts Lecture 44 Creating Bar Charts, Boxplots, 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 Perfog 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 ing and Installing R Lecture 57 ing 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 Perfog Correlations and Multivariate Analysis Lecture 76 Operating Linear Regression and Multivariate Analysis Lecture 77 Conducting an Exact Binomial Test Lecture 78 Perfog Student's t-test Lecture 79 Perfog 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 Perfog 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 Naive 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 Perfog Cross Validation with the e1071 Package Lecture 120 Perfog Cross Validation with the caret Package Lecture 121 Ranking the Variable Importance with the caret Package Lecture 122 Ranking the Variable Importance with the er 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 Perfog Cross Validation with the Bagging Method Lecture 132 Classifying Data with the Boosting Method Lecture 133 Perfog Cross Validation with the Boosting Method Lecture 134 Classifying Data with Gradient Boosting Lecture 135 Calculating the Mas 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 Transfog 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 Perfog Feature Selection with FSelector Lecture 159 Perfog Dimension Reduction with PCA Lecture 160 Deteing the Number of Principal Components Using the Scree Test Lecture 161 Deteing the Number of Principal Components Using the Kaiser Method Lecture 162 Visualizing Multivariate Data Using biplot Lecture 163 Perfog Dimension Reduction with MDS Lecture 164 Reducing Dimensions with SVD Lecture 165 Compressing Images with SVD Lecture 166 Perfog Nonlinear Dimension Reduction with ISOMAP Lecture 167 Perfog 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 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 eeers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R HomePage:
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