R Machine Learning With R - Beginner To Expert! 4-In-1

What you'll learn
Process a classic dataset, from data cleaning to presenting results with effective graphics.
Evaluate the performance of your models and put your model into use.
Explore advanced techniques such as hyper parameter tuning and deep learning.
Incorporate R and Hadoop to solve machine learning problems on big data.
Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees.
Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm.
Get to know hyper-parameter tuning by exploring and iterating through parameters
Requirements
Prior basic knowledge of R programming language is assumed.
Basic understanding of Machine Learning concepts, data frames and statistics would be useful (not mandatory).
Description
Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. R language is widely used among statisticians and data miners to develop statistical software and perform data analysis. It provides a cutting-edge power you need to work with Machine Learning techniques. This comprehensive 4-in-1 is a step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language. Apply R to simple predictive modeling with short and simple code. Dive into the advanced algorithms such as hyper-parameter tuning and DeepLearning, and putting your models into production!By the end of this course, you'll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R!Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Getting Started with Machine Learning in R, covers learning Machine learning techniques in the popular statistical language R. The course will take you through some different types of ML. You'll work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. You'll explore algorithms like Random Forest and Naive Bayes for working on your data in R. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then you'll have a chance to do it yourself on another data set.By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs.The second course, Advanced Machine Learning with R, covers advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples. In this course, you'll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you'll learn all about neural networks through an example of DNA classification data. You'll explore networks, implement them, and classify them. After that, you'll see how to tune hyper-parameters using a data set of sonar data and you'll get to know their properties. Next, you'll understand unsupervised learning with an example of clustering politicians, where you'll explore new patterns, understand unsupervised learning, and visualize and cluster the data.The third course, R Machine Learning solutions, covers building powerful predictive models in R. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You'll then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction.The fourth course, Applied Machine Learning and Deep Learning with R covers building powerful machine learning and deep learning applications with help of the R programming language and its various packages. In this course, you'll examine in detail the R software, which is the most popular statistical programming language of recent years. Explore different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you'll dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you'll learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.By the end of this course, you'll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R.About the AuthorsPhil Rennertis a Principal Research Engineer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challenging technical problems, innovating new techniques where existing ones don't apply. He is extensively skilled in machine learning, natural language processing, and data mining.Tim Hoolihancurrently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group. In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Personally, he enjoys working on practice problems on Kaggle .com as well. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.Yu-Wei, Chiu (David Chiu) is the founder of LargitData Company. He has previously worked for Trend Micro as a software engineer, with the responsibility of building up 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 to data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, a book compiled for Packt Publishing.Olgun is PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. Also working as Data Scientist.He is so familiar with Big Data technologies like Hadoop, Spark and able to use Hive, Impala. He is a big fan of R. Also he really loves to work with Shiny, SparkR. He has many academic papers and proceedings about applications of statistics on different disciplines. Mr. Olgun really loves statistic and loves to investigate new methods, share his experience with people.
Overview
Section 1: Getting Started with Machine Learning with R
Lecture 1 The Course Overview
Lecture 2 Your R Environment
Lecture 3 Exploring the US Arrests Dataset
Lecture 4 Creating Test and Train Datasets
Lecture 5 Creating a Linear Regression Model
Lecture 6 Scoring on the Test Set
Lecture 7 Plotting the Test Results
Lecture 8 EDA: mtcars
Lecture 9 Working with Factors
Lecture 10 Scaling Data
Lecture 11 Creating a Classification Model
Lecture 12 Advanced Formulas
Lecture 13 Precision, Recall, and F-Score
Lecture 14 Introduction to Caret
Lecture 15 EDA and Preprocessing
Lecture 16 Preparing Test and Train Datasets
Lecture 17 Creating a Model
Lecture 18 Cross Validation
Lecture 19 F-Score
Section 2: Advanced Machine Learning with R
Lecture 20 The Course Overview
Lecture 21 Explore Sonar Data Set
Lecture 22 Tuning Grids
Lecture 23 Iterating – Improving our Tuning
Lecture 24 Final Results
Lecture 25 Neural Networks Basics
Lecture 26 Explore the DNA Set
Lecture 27 Implement a Neural Network
Lecture 28 Multi-layer Perceptron
Lecture 29 One Hot Encoding and MLP
Lecture 30 Overview of the Keras
Lecture 31 Installing Keras
Lecture 32 Neural Network in Keras
Lecture 33 CIFAR10 Data Set
Lecture 34 Convolutional Neural Network
Lecture 35 Saving Your Model in R
Lecture 36 Saving Your Model for Another Language
Lecture 37 Shiny Web Interfaces
Lecture 38 Wrapping Your Model in Shiny
Section 3: R Machine Learning solutions
Lecture 39 The Course Overview
Lecture 40 Downloading and Installing R
Lecture 41 Downloading and Installing RStudio
Lecture 42 Installing and Loading Packages
Lecture 43 Reading and Writing Data
Lecture 44 Using R to Manipulate Data
Lecture 45 Applying Basic Statistics
Lecture 46 Visualizing Data
Lecture 47 Getting a Dataset for Machine Learning
Lecture 48 Reading a Titanic Dataset from a CSV File
Lecture 49 Converting Types on Character Variables
Lecture 50 Detecting Missing Values
Lecture 51 Imputing Missing Values
Lecture 52 Exploring and Visualizing Data
Lecture 53 Predicting Passenger Survival with a Decision Tree
Lecture 54 Validating the Power of Prediction with a Confusion Matrix
Lecture 55 Assessing performance with the ROC curve
Lecture 56 Understanding Data Sampling in R
Lecture 57 Operating a Probability Distribution in R
Lecture 58 Working with Univariate Descriptive Statistics in R
Lecture 59 Performing Correlations and Multivariate Analysis
Lecture 60 Operating Linear Regression and Multivariate Analysis
Lecture 61 Conducting an Exact Binomial Test
Lecture 62 Performing Student's t-test
Lecture 63 Performing the Kolmogorov-Smirnov Test
Lecture 64 Understanding the Wilcoxon Rank Sum and Signed Rank Test
Lecture 65 Working with Pearson's Chi-Squared Test
Lecture 66 Conducting a One-Way ANOVA
Lecture 67 Performing a Two-Way ANOVA
Lecture 68 Fitting a Linear Regression Model with lm
Lecture 69 Summarizing Linear Model Fits
Lecture 70 Using Linear Regression to Predict Unknown Values
Lecture 71 Generating a Diagnostic Plot of a Fitted Model
Lecture 72 Fitting a Polynomial Regression Model with lm
Lecture 73 Fitting a Robust Linear Regression Model with rlm
Lecture 74 Studying a case of linear regression on SLID data
Lecture 75 Applying the Gaussian Model for Generalized Linear Regression
Lecture 76 Applying the Poisson model for Generalized Linear Regression
Lecture 77 Applying the Binomial Model for Generalized Linear Regression
Lecture 78 Fitting a Generalized Additive Model to Data
Lecture 79 Visualizing a Generalized Additive Model
Lecture 80 Diagnosing a Generalized Additive Model
Lecture 81 Preparing the Training and Testing Datasets
Lecture 82 Building a Classification Model with Recursive Partitioning Trees
Lecture 83 Visualizing a Recursive Partitioning Tree
Lecture 84 Measuring the Prediction Performance of a Recursive Partitioning Tree
Lecture 85 Pruning a Recursive Partitioning Tree
Lecture 86 Building a Classification Model with a Conditional Inference Tree
Lecture 87 Visualizing a Conditional Inference Tree
Lecture 88 Measuring the Prediction Performance of a Conditional Inference Tree
Lecture 89 Classifying Data with the K-Nearest Neighbor Classifier
Lecture 90 Classifying Data with Logistic Regression
Lecture 91 Classifying data with the Naīve Bayes Classifier
Lecture 92 Classifying Data with a Support Vector Machine
Lecture 93 Choosing the Cost of an SVM
Lecture 94 Visualizing an SVM Fit
Lecture 95 Predicting Labels Based on a Model Trained by an SVM
Lecture 96 Tuning an SVM
Lecture 97 Training a Neural Network with neuralnet
Lecture 98 Visualizing a Neural Network Trained by neuralnet
Lecture 99 Predicting Labels based on a Model Trained by neuralnet
Lecture 100 Training a Neural Network with nnet
Lecture 101 Predicting labels based on a model trained by nnet
Lecture 102 Estimating Model Performance with k-fold Cross Validation
Lecture 103 Performing Cross Validation with the e1071 Package
Lecture 104 Performing Cross Validation with the caret Package
Lecture 105 Ranking the Variable Importance with the caret Package
Lecture 106 Ranking the Variable Importance with the rminer Package
Lecture 107 Finding Highly Correlated Features with the caret Package
Lecture 108 Selecting Features Using the Caret Package
Lecture 109 Measuring the Performance of the Regression Model
Lecture 110 Measuring Prediction Performance with a Confusion Matrix
Lecture 111 Measuring Prediction Performance Using ROCR
Lecture 112 Comparing an ROC Curve Using the Caret Package
Lecture 113 Measuring Performance Differences between Models with the caret Package
Lecture 114 Classifying Data with the Bagging Method
Lecture 115 Performing Cross Validation with the Bagging Method
Lecture 116 Classifying Data with the Boosting Method
Lecture 117 Performing Cross Validation with the Boosting Method
Lecture 118 Classifying Data with Gradient Boosting
Lecture 119 Calculating the Margins of a Classifier
Lecture 120 Calculating the Error Evolution of the Ensemble Method
Lecture 121 Classifying Data with Random Forest
Lecture 122 Estimating the Prediction Errors of Different Classifiers
Lecture 123 Clustering Data with Hierarchical Clustering
Lecture 124 Cutting Trees into Clusters
Lecture 125 Clustering Data with the k-Means Method
Lecture 126 Drawing a Bivariate Cluster Plot
Lecture 127 Comparing Clustering Methods
Lecture 128 Extracting Silhouette Information from Clustering
Lecture 129 Obtaining the Optimum Number of Clusters for k-Means
Lecture 130 Clustering Data with the Density-Based Method
Lecture 131 Clustering Data with the Model-Based Method
Lecture 132 Visualizing a Dissimilarity Matrix
Lecture 133 Validating Clusters Externally
Lecture 134 Transforming Data into Transactions
Lecture 135 Displaying Transactions and Associations
Lecture 136 Mining Associations with the Apriori Rule
Lecture 137 Pruning Redundant Rules
Lecture 138 Visualizing Association Rules
Lecture 139 Mining Frequent Itemsets with Eclat
Lecture 140 Creating Transactions with Temporal Information
Lecture 141 Mining Frequent Sequential Patterns with cSPADE
Lecture 142 Performing Feature Selection with FSelector
Lecture 143 Performing Dimension Reduction with PCA
Lecture 144 Determining the Number of Principal Components Using the Scree Test
Lecture 145 Determining the Number of Principal Components Using the Kaiser Method
Lecture 146 Visualizing Multivariate Data Using biplot
Lecture 147 Performing Dimension Reduction with MDS
Lecture 148 Reducing Dimensions with SVD
Lecture 149 Compressing Images with SVD
Lecture 150 Performing Nonlinear Dimension Reduction with ISOMAP
Lecture 151 Performing Nonlinear Dimension Reduction with Local Linear Embedding
Lecture 152 Preparing the RHadoop Environment
Lecture 153 Installing rmr2
Lecture 154 Installing rhdfs
Lecture 155 Operating HDFS with rhdfs
Lecture 156 Implementing a Word Count Problem with RHadoop
Lecture 157 Comparing the Performance between an R MapReduce Program & a Standard R Program
Lecture 158 Testing and Debugging the rmr2 Program
Lecture 159 Installing plyrmr
Lecture 160 Manipulating Data with plyrmr
Lecture 161 Conducting Machine Learning with RHadoop
Lecture 162 Configuring RHadoop Clusters on Amazon EMR
Section 4: Applied Machine Learning and Deep Learning with R
Lecture 163 The Course Overview
Lecture 164 Supervised and Unsupervised Learning
Lecture 165 Feature Selection
Lecture 166 Model Evaluation Methods - Cross Validation
Lecture 167 Performance Metrics
Lecture 168 K-Means Clustering
Lecture 169 Hierarchical Clustering
Lecture 170 DBSCAN Algorithm
Lecture 171 Clustering Exercises with R
Lecture 172 Dealing with Problems About Clustering
Lecture 173 k-NN Classification
Lecture 174 Logistic Regression
Lecture 175 Naive Bayes
Lecture 176 Decision Trees
Lecture 177 Classification Exercises with R
Lecture 178 Handling Problems About Classification
Lecture 179 Introduction to Artificial Neural Networks
Lecture 180 Types of Artificial Neural Networks
Lecture 181 Back Propagation
Lecture 182 Artificial Neural Networks Exercises with R
Lecture 183 Tricks for ANN in R
Lecture 184 What Is Deep Learning?
Lecture 185 Elements of Deep Neural Networks
Lecture 186 Types of Deep Neural Networks
Lecture 187 Introduction to Deep Learning Frameworks
Lecture 188 Exercises with TensorFlow in R
Lecture 189 Tricks About Application of Deep Neural Nets
Lecture 190 Introduction to SparkR
Lecture 191 Installation of SparkR
Lecture 192 Writing First Script on SparkR
Lecture 193 Generalized Linear Models with SparkR
Lecture 194 Classification Exercises with SparkR
Lecture 195 Clustering Exercises with SparkR
Lecture 196 Naive Bayes with SparkR
Lecture 197 Tricks About SparkR
An aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, who wants to explore the advanced topics in machine learning with R with examples to build powerful predictive models in R!,Anyone who wants to enter the world of machine learning and is looking for a guide that is easy to follow.


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