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Learning Path: R: Complete Machine Learning and Deep Learning Solutions

Video Introducing this tutorial

Chapter 1 : Mastering R Programming
The Course Overview 00:07:45
Performing Univariate Analysis 00:05:22
Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA 00:05:43
Detecting and Treating Outlier 00:03:21
Treating Missing Values with `mice` 00:03:59
Building Linear Regressors 00:07:35
Interpreting Regression Results and Interactions Terms 00:05:19
Performing Residual Analysis and Extracting Extreme Observations With Cook's Distance 00:03:25
Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA 00:04:39
Validating Model Performance on New Data with k-Fold Cross Validation 00:02:29
Building Non-Linear Regressors with Splines and GAMs 00:05:20
Building Logistic Regressors, Evaluation Metrics, and ROC Curve 00:12:38
Understanding the Concept and Building Naive Bayes Classifier 00:09:24
Building k-Nearest Neighbors Classifier 00:07:01
Building Tree Based Models Using RPart, cTree, and C5.0 00:06:33
Building Predictive Models with the caret Package 00:08:11
Selecting Important Features with RFE, varImp, and Boruta 00:05:19
Building Classifiers with Support Vector Machines 00:08:04
Understanding Bagging and Building Random Forest Classifier 00:05:07
Implementing Stochastic Gradient Boosting with GBM 00:05:18
Regularization with Ridge, Lasso, and Elasticnet 00:08:53
Building Classifiers and Regressors with XGBoost 00:10:10
Dimensionality Reduction with Principal Component Analysis 00:05:05
Clustering with k-means and Principal Components 00:03:16
Determining Optimum Number of Clusters 00:05:25
Understanding and Implementing Hierarchical Clustering 00:02:36
Clustering with Affinity Propagation 00:05:25
Building Recommendation Engines 00:09:01
Understanding the Components of a Time Series, and the xts Package 00:05:42
Stationarity, De-Trend, and De-Seasonalize 00:04:07
Understanding the Significance of Lags, ACF, PACF, and CCF 00:03:49
Forecasting with Moving Average and Exponential Smoothing 00:02:25
Forecasting with Double Exponential and Holt Winters 00:03:23
Forecasting with ARIMA Modelling 00:05:26
Scraping Web Pages and Processing Texts 00:09:24
In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data. 00:09:07
Cosine Similarity and Latent Semantic Analysis 00:07:20
Extracting Topics with Latent Dirichlet Allocation 00:05:07
Sentiment Scoring with tidytext and Syuzhet 00:04:23
Classifying Texts with RTextTools 00:03:57
Building a Basic ggplot2 and Customizing the Aesthetics and Themes 00:07:18
Manipulating Legend, AddingText, and Annotation 00:03:31
Drawing Multiple Plots with Faceting and Changing Layouts 00:03:18
Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots 00:05:25
ggplot2 Extensions and ggplotly 00:03:11
Implementing Best Practices to Speed Up R Code 00:05:47
Implementing Parallel Computing with doParallel and foreach 00:04:22
Writing Readable and Fast R Code with Pipes and DPlyR 00:05:40
Writing Super Fast R Code with Minimal Keystrokes Using Data.Table 00:06:38
Interface C++ in R with RCpp 00:11:09
Understanding the Structure of an R Package 00:05:02
Build, Document, and Host an R Package on GitHub 00:07:10
Performing Important Checks Before Submitting to CRAN 00:04:06
Submitting an R Package to CRAN 00:03:11

Chapter 2 : R Machine Learning solutions
The Course Overview 00:04:38
Downloading and Installing R 00:06:10
Downloading and Installing RStudio 00:03:10
Installing and Loading Packages 00:05:46
Reading and Writing Data 00:05:54
Using R to Manipulate Data 00:05:47
Applying Basic Statistics 00:04:47
Visualizing Data 00:03:33
Getting a Dataset for Machine Learning 00:02:39
Reading a Titanic Dataset from a CSV File 00:08:36
Converting Types on Character Variables 00:03:05
Detecting Missing Values 00:03:19
Missing values affect the inference of a dataset. Thus it is important to detect them. 00:04:31
Exploring and Visualizing Data 00:04:25
Predicting Passenger Survival with a Decision Tree 00:03:59
Validating the Power of Prediction with a Confusion Matrix 00:02:08
Assessing performance with the ROC curve 00:02:33
Understanding Data Sampling in R 00:03:31
Operating a Probability Distribution in R 00:05:42
Working with Univariate Descriptive Statistics in R 00:05:10
Performing Correlations and Multivariate Analysis 00:03:02
Operating Linear Regression and Multivariate Analysis 00:03:25
Conducting an Exact Binomial Test 00:03:48
Performing Student's t-test 00:03:13
Performing the Kolmogorov-Smirnov Test 00:04:43
Understanding the Wilcoxon Rank Sum and Signed Rank Test 00:02:04
Working with Pearson's Chi-Squared Test 00:05:09
Conducting a One-Way ANOVA 00:04:16
Performing a Two-Way ANOVA 00:04:02
Fitting a Linear Regression Model with lm 00:04:53
Summarizing Linear Model Fits 00:05:21
Using Linear Regression to Predict Unknown Values 00:02:51
Generating a Diagnostic Plot of a Fitted Model 00:03:58
Fitting a Polynomial Regression Model with lm 00:02:16
Fitting a Robust Linear Regression Model with rlm 00:02:16
Studying a case of linear regression on SLID data 00:06:39
Reducing Dimensions with SVD 00:02:11
Applying the Poisson model for Generalized Linear Regression 00:01:34
Applying the Binomial Model for Generalized Linear Regression 00:02:02
Fitting a Generalized Additive Model to Data 00:03:14
Visualizing a Generalized Additive Model 00:01:27
Diagnosing a Generalized Additive Model 00:03:38
Preparing the Training and Testing Datasets 00:03:45
Building a Classification Model with Recursive Partitioning Trees 00:06:10
Visualizing a Recursive Partitioning Tree 00:03:04
Measuring the Prediction Performance of a Recursive Partitioning Tree 00:02:49
Pruning a Recursive Partitioning Tree 00:02:38
Building a Classification Model with a Conditional Inference Tree 00:01:56
Visualizing a Conditional Inference Tree 00:02:38
Measuring the Prediction Performance of a Conditional Inference Tree 00:02:10
Classifying Data with the K-Nearest Neighbor Classifier 00:05:31
Classifying Data with Logistic Regression 00:04:38
Classifying data with the Naïve Bayes Classifier 00:06:16
Classifying Data with a Support Vector Machine 00:05:58
Choosing the Cost of an SVM 00:02:57
Visualizing an SVM Fit 00:03:33
Predicting Labels Based on a Model Trained by an SVM 00:03:49
Tuning an SVM 00:02:48
Training a Neural Network with neuralnet 00:04:08
Visualizing a Neural Network Trained by neuralnet 00:02:22
Predicting Labels based on a Model Trained by neuralnet 00:03:07
Training a Neural Network with nnet 00:02:46
Predicting labels based on a model trained by nnet 00:02:49
Estimating Model Performance with k-fold Cross Validation 00:03:42
Performing Cross Validation with the e1071 Package 00:03:22
Performing Cross Validation with the caret Package 00:02:59
Ranking the Variable Importance with the caret Package 00:02:21
Ranking the Variable Importance with the rminer Package 00:02:30
Finding Highly Correlated Features with the caret Package 00:02:13
Selecting Features Using the Caret Package 00:04:59
Measuring the Performance of the Regression Model 00:03:58
Measuring Prediction Performance with a Confusion Matrix 00:02:07
Measuring Prediction Performance Using ROCR 00:02:46
Comparing an ROC Curve Using the Caret Package 00:03:44
Measuring Performance Differences between Models with the caret Package 00:03:41
Classifying Data with the Bagging Method 00:07:53
Performing Cross Validation with the Bagging Method 00:01:56
Classifying Data with the Boosting Method 00:06:05
Performing Cross Validation with the Boosting Method 00:02:06
Classifying Data with Gradient Boosting 00:07:10
Calculating the Margins of a Classifier 00:05:30
Calculating the Error Evolution of the Ensemble Method 00:02:19
Classifying Data with Random Forest 00:07:02
Estimating the Prediction Errors of Different Classifiers 00:04:35
Clustering Data with Hierarchical Clustering 00:08:40
Cutting Trees into Clusters 00:03:30
Clustering Data with the k-Means Method 00:04:10
Drawing a Bivariate Cluster Plot 00:03:32
Comparing Clustering Methods 00:04:16
Extracting Silhouette Information from Clustering 00:02:40
Obtaining the Optimum Number of Clusters for k-Means 00:02:49
Clustering Data with the Density-Based Method 00:06:42
Clustering Data with the Model-Based Method 00:04:38
Visualizing a Dissimilarity Matrix 00:03:24
Validating Clusters Externally 00:04:12
Transforming Data into Transactions 00:03:35
Displaying Transactions and Associations 00:02:14
Mining Associations with the Apriori Rule 00:07:24
Pruning Redundant Rules 00:02:26
Visualizing Association Rules 00:05:07
Mining Frequent Itemsets with Eclat 00:03:36
Creating Transactions with Temporal Information 00:02:41
Mining Frequent Sequential Patterns with cSPADE 00:04:16
Performing Feature Selection with FSelector 00:07:38
Performing Dimension Reduction with PCA 00:07:19
Determining the Number of Principal Components Using the Scree Test 00:03:34
Determining the Number of Principal Components Using the Kaiser Method 00:02:05
Visualizing Multivariate Data Using biplot 00:03:17
Performing Dimension Reduction with MDS 00:05:38
Reducing Dimensions with SVD 00:03:19
Compressing Images with SVD 00:03:05
Performing Nonlinear Dimension Reduction with ISOMAP 00:04:34
Performing Nonlinear Dimension Reduction with Local Linear Embedding 00:04:55
Preparing the RHadoop Environment 00:05:36
Installing rmr2 00:03:53
Installing rhdfs 00:04:15
Operating HDFS with rhdfs 00:05:47
Implementing a Word Count Problem with RHadoop 00:05:27
Comparing the Performance between an R MapReduce Program and a Standard R Program 00:05:03
Testing and Debugging the rmr2 Program 00:03:49
Installing plyrmr 00:03:12
Manipulating Data with plyrmr 00:03:52
Conducting Machine Learning with RHadoop 00:04:39
Configuring RHadoop Clusters on Amazon EMR 00:05:28

Chapter 3 : Deep Learning with R
The Course Overview 00:05:22
Fundamental Concepts in Deep Learning 00:07:43
Introduction to Artificial Neural Networks 00:07:58
Classification with Two-Layers Artificial Neural Networks 00:08:03
Probabilistic Predictions with Two-Layer ANNs 00:06:33
Introduction to Multi-hidden-layer Architectures 00:04:31
Tuning ANNs Hyper-Parameters and Best Practices 00:06:12
Neural Network Architectures 00:04:58
Neural Network Architectures Continued 00:08:02
The LearningProcess 00:05:36
Optimization Algorithms and Stochastic Gradient Descent 00:08:11
Backpropagation 00:06:44
Hyper-Parameters Optimization 00:07:18
Introduction to Convolutional Neural Networks 00:09:57
Introduction to Convolutional Neural Networks Continued 00:10:36
CNNs in R 00:10:41
Classifying Real-World Images with Pre-Trained Models 00:08:29
Introduction to Recurrent Neural Networks 00:11:58
Introduction to Long Short-Term Memory 00:08:08
RNNs in R 00:08:55
Use-Case – Learning How to Spell English Words from Scratch 00:06:35
Introduction to Unsupervised and Reinforcement Learning 00:06:45
Autoencoders 00:04:57
Restricted Boltzmann Machines and Deep Belief Networks 00:07:45
Reinforcement Learning with ANNs 00:07:23
Use-Case – Anomaly Detection through Denoising Autoencoders 00:06:53
Deep Learning for Computer Vision 00:07:20
Deep Learning for Natural Language Processing 00:06:05
Deep Learning for Audio Signal Processing 00:05:02
Deep Learning for Complex Multimodal Tasks 00:04:32
Other Important Applications of Deep Learning 00:05:24
Debugging Deep Learning Systems 00:05:56
GPU and MGPU Computing for Deep Learning 00:04:57
A Complete Comparison of Every DL Packages in R 00:04:41
Research Directions and Open Questions 00:04:48