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

    Posted By: ELK1nG
    R: Machine Learning With R - Beginner To Expert!: 4-In-1

    R: Machine Learning With R - Beginner To Expert!: 4-In-1
    Last updated 9/2018
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 6.35 GB | Duration: 13h 27m

    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 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.