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Explore the advanced topics in Machine Learning with R in a step by step manner to build powerful predictive models in R

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 Eeer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challeg 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 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 LaData Company. He has previously worked for Trend Micro as a software eeer, 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 acad 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 ing and Installing R

Lecture 41 ing 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 Perfog Correlations and Multivariate Analysis

Lecture 60 Operating Linear Regression and Multivariate Analysis

Lecture 61 Conducting an Exact Binomial Test

Lecture 62 Perfog Student's t-test

Lecture 63 Perfog 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 Perfog 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 Naive 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 Perfog Cross Validation with the e1071 Package

Lecture 104 Perfog Cross Validation with the caret Package

Lecture 105 Ranking the Variable Importance with the caret Package

Lecture 106 Ranking the Variable Importance with the er 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 Perfog Cross Validation with the Bagging Method

Lecture 116 Classifying Data with the Boosting Method

Lecture 117 Perfog Cross Validation with the Boosting Method

Lecture 118 Classifying Data with Gradient Boosting

Lecture 119 Calculating the Mas 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 Transfog 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 Perfog Feature Selection with FSelector

Lecture 143 Perfog Dimension Reduction with PCA

Lecture 144 Deteing the Number of Principal Components Using the Scree Test

Lecture 145 Deteing the Number of Principal Components Using the Kaiser Method

Lecture 146 Visualizing Multivariate Data Using biplot

Lecture 147 Perfog Dimension Reduction with MDS

Lecture 148 Reducing Dimensions with SVD

Lecture 149 Compressing Images with SVD

Lecture 150 Perfog Nonlinear Dimension Reduction with ISOMAP

Lecture 151 Perfog 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 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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