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Working in a hands-on learning environment, led by our ML expert instructor, students will learn about and explore: Apply R to simplify predictive modeling with short and simple code. Use machine learning to solve problems ranging from small to big data. Build a training and testing dataset, applying different classification methods.

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January 6, 2021

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Certification

Course Description

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

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Course Curriculum

    • Practical Machine Learning with R 00:00:00
    • 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
    • Understanding of basic data structures 00:00:00
    • Basic commands for subsetting 00:00:00
    • Reading and writing data 00:00:00
    • Manipulating 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 Air Quality Datasets 00:00:00
    • Introduction 00:00:00
    • Using air quality dataset 00:00:00
    • Converting attributes to factor 00:00:00
    • Detecting missing values 00:00:00
    • Imputing missing values 00:00:00
    • Exploring and visualizing data 00:00:00
    • Predicting values from datasets 00:00:00
    • Analyzing Time Series Data 00:00:00
    • Introduction 00:00:00
    • Looking at time series data 00:00:00
    • Plotting and forecasting time series data 00:00:00
    • Extracting, subsetting, merging, filling, and padding 00:00:00
    • Successive differences and moving averages 00:00:00
    • Exponential smoothing 00:00:00
    • Plotting the autocorrelation function 00:00:00
    • R and Statistics 00:00:00
    • Introduction 00:00:00
    • 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
    • Conducting an exact binomial test 00:00:00
    • Performing a 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 00:00:00
    • Introduction 00:00:00
    • Different types of regression 00:00:00
    • 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 multiple regression 00:00:00
    • Summarizing multiple regression 00:00:00
    • Using multiple regression to predict unknown values 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
    • Survival Analysis 00:00:00
    • Introduction 00:00:00
    • Loading and observing data 00:00:00
    • Viewing the summary of survival analysis 00:00:00
    • Visualizing the Survival Curve 00:00:00
    • Using the log-rank test 00:00:00
    • Using the COX proportional hazard model 00:00:00
    • Nelson-Aalen Estimator of cumulative hazard 00:00:00
    • Classification 1 – Tree, Lazy, and Probabilistic 00:00:00
    • Introduction 00:00:00
    • 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
    • Handling missing data and split and surrogate variables 00:00:00
    • Building a classification model with a conditional inference tree 00:00:00
    • Control parameters in conditional inference trees 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 2 – Neural Network and SVM 00:00:00
    • Introduction 00:00:00
    • Classifying data with a support vector machine 00:00:00
    • Choosing the cost of a support vector machine 00:00:00
    • Visualizing an SVM fit 00:00:00
    • Predicting labels based on a model trained by a support vector machine 00:00:00
    • Tuning a support vector machine 00:00:00
    • The basics of neural network 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 00:00:00
    • Introduction 00:00:00
    • Estimating model performance with k-fold cross-validation 00:00:00
    • Estimating model performance with Leave One Out 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 00:00:00
    • Introduction 00:00:00
    • Using the Super Learner algorithm 00:00:00
    • Using ensemble to train and test 00:00:00
    • 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 00:00:00
    • Introduction 00:00:00
    • 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 00:00:00
    • Introduction 00:00:00
    • 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
    • Using the TraMineR package for sequence analysis 00:00:00
    • Visualizing sequence, Chronogram, and Traversal Statistics 00:00:00
    • Dimension Reduction 00:00:00
    • Introduction 00:00:00
    • Why to reduce the dimension? 00:00:00
    • 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 multivariate 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 (R and Hadoop) 00:00:00
    • Introduction 00:00:00
    • 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 program 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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