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Machine Learning with R · LearnSpace
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Machine Learning with R

Курс от Packt
Средний≈ 34.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Machine Learning with R provides a thorough introduction to machine learning techniques using the R programming language, focusing on practical applications. You'll gain the skills necessary for preparing data, evaluating models, and applying advanced methods such as ensemble learning and deep learning. This course bridges the gap between theory and real-world applications, ensuring you not only understand the concepts but also know how to implement them in real scenarios. By working with tools like Spark and Hadoop, you will gain experience with big data and develop a comprehensive understanding of the machine learning process. This course stands out by offering a hands-on, interactive approach to mastering machine learning, making it suitable for learners who want to dive into the field. Whether you are just starting out or looking to refine your skills, the course provides a structured learning path to achieve practical, measurable outcomes. By the end of this course, you will be confident in building and deploying machine learning models using R. Ideal for those starting out in data science, this course requires basic knowledge of statistics and programming but does not require prior R experience. It is a perfect fit for learners aiming to enhance their machine learning skills. Based on the book, Machine Learning with R, by Brett Lantz.

Навыки, которые вы освоите

Deep LearningKeras (Neural Network Library)R (Software)Data ScienceApache HadoopData ManipulationData AnalysisMachine LearningUnsupervised LearningMachine Learning AlgorithmsModel TrainingClassification AlgorithmsTidyverse (R Package)R ProgrammingApache SparkData WranglingBayesian NetworkLogistic RegressionApplied Machine LearningModel Evaluation

Программа курса

15 модулей · 220 учебных материалов

01Introducing Machine Learning14 материалов

Lesson 1

Introduction - Overview VideoВидеоIntroducing Machine Learning - Overview VideoВидеоIntroductionЧтениеUses and Abuses of Machine LearningЧтение

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Packt - Course Instructors

Преподаватель курса

Machine Learning with R
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 34.7 ч

15 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
The Limits of Machine LearningЧтение
NoteЧтение
How Machines LearnЧтение
AbstractionЧтение
GeneralizationЧтение
EvaluationЧтение
Types of Machine Learning AlgorithmsЧтение
Matching Input Data to AlgorithmsЧтение
Why R and Why R NowЧтение
Foundations of Machine LearningЗадание
02Managing and Understanding Data15 материалов

Lesson 1

Managing and Understanding Data - Overview VideoВидеоIntroductionЧтениеFactorsЧтениеListsЧтениеData FramesЧтениеMatrices and ArraysЧтениеImporting and Saving Datasets from CSV FilesЧтениеExploring and Understanding DataЧтениеMeasuring the Central Tendency Mean and MedianЧтениеMeasuring Spread Quartiles and the Five-Number SummaryЧтениеUnderstanding Numeric Data Uniform and Normal DistributionsЧтениеExploring Categorical FeaturesЧтениеVisualizing Relationships ScatterplotsЧтениеExamining Relationships Two-Way Cross-TabulationsЧтениеData Analysis FundamentalsЗадание
03Lazy Learning Classification Using Nearest Neighbors9 материалов

Lesson 1

Lazy Learning Classification Using Nearest Neighbors - Overview VideoВидеоIntroductionЧтениеMeasuring Similarity with DistanceЧтениеPreparing Data for Use with k-NNЧтениеWhy Is the k-NN Algorithm Lazy?ЧтениеExploring and Preparing the DataЧтениеData Preparation Creating Training and Test DatasetsЧтениеEvaluating Model PerformanceЧтениеExploring Lazy Learning and Its Core PrinciplesЗадание
04Probabilistic Learning Classification Using Naive Bayes13 материалов

Lesson 1

Probabilistic Learning Classification Using Naive Bayes - Overview VideoВидеоIntroductionЧтениеUnderstanding Joint ProbabilityЧтениеComputing Conditional Probability with Bayes' TheoremЧтениеStrengths WeaknessesЧтениеThe Laplace EstimatorЧтениеExample Filtering Mobile Phone Spam With the Naive Bayes AlgorithmЧтениеExploring and Preparing the DataЧтениеData Preparation: Splitting Text Documents Into WordsЧтениеVisualizing Text Data Word CloudsЧтениеData Preparation Creating Indicator Features for Frequent WordsЧтениеEvaluating Model PerformanceЧтениеProbabilistic Learning FundamentalsЗадание
05Divide and Conquer Classification Using Decision Trees and Rules12 материалов

Lesson 1

Divide and Conquer Classification Using Decision Trees and Rules - Overview VideoВидеоThe C5.0 Decision Tree AlgorithmЧтениеPruning the Decision TreeЧтениеData Preparation Creating Random Training and Test DatasetsЧтениеTraining a Model on the DataЧтениеEvaluating Model PerformanceЧтениеMaking Some Mistakes Cost More Than OthersЧтениеSeparate and ConquerЧтениеThe 1R AlgorithmЧтениеRules from Decision TreesЧтениеCollecting DataЧтениеMachine Learning Fundamentals and Decision Tree PrinciplesЗадание
06Forecasting Numeric Data Regression Methods22 материалов

Lesson 1

Forecasting Numeric Data Regression Methods - Overview VideoВидеоIntroductionЧтениеSimple Linear RegressionЧтениеOrdinary Least Squares EstimationЧтениеCorrelationsЧтениеGeneralized Linear Models and Logistic RegressionЧтениеTableЧтениеExample Predicting Auto Insurance Claims Costs Using Linear RegressionЧтениеExploring and Preparing the DataЧтениеVisualizing Relationships Between Features with the Scatterplot MatrixЧтениеPractice: Choose the Right Regression ModelDIALOGUETraining a Model on the DataЧтениеEvaluating Model PerformanceЧтениеModel Specification Adding Interaction EffectsЧтениеMaking Predictions with a Regression ModelЧтениеGoing Further Predicting Insurance Policyholder Churn With Logistic RegressionЧтениеUnderstanding Regression Trees and Model TreesЧтениеEstimating the Quality of Wines With Regression Trees and Model TreesЧтениеExploring and Preparing the DataЧтениеVisualizing Decision TreesЧтениеImproving Model PerformanceЧтениеForecasting and Model Evaluation FundamentalsЗадание
07Black-Box Methods: Neural Networks and Support Vector Machines17 материалов

Lesson 1

Black-Box Methods: Neural Networks and Support Vector Machines - Overview VideoВидеоIntroductionЧтениеFrom Biological to Artificial NeuronsЧтениеNetwork TopologyЧтениеThe Direction of Information TravelЧтениеThe Number of Nodes in Each LayerЧтениеPractice: Analyze Neural Network Design Trade-offsDIALOGUEForward and Backward PhasesЧтениеTraining a Model on the DataЧтениеImproving Model PerformanceЧтениеUnderstanding Support Vector MachinesЧтениеThe Case of Linearly Separable DataЧтениеUsing Kernels for Nonlinear SpacesЧтениеExample Performing OCR with SVMsЧтениеTraining a Model on the DataЧтениеImproving Model PerformanceЧтениеExploring Machine Learning Techniques and ChallengesЗадание
08Finding Patterns: Market Basket Analysis Using Association Rules11 материалов

Lesson 1

Finding Patterns: Market Basket Analysis Using Association Rules - Overview VideoВидеоIntroductionЧтениеThe Apriori Algorithm for Association Rule LearningЧтениеMeasuring Rule Interest Support and ConfidenceЧтениеExample: Identifying Frequently Purchased Groceries With Association RulesЧтениеVisualizing Item Support Item Frequency PlotsЧтениеTraining a Model on the DataЧтениеEvaluating Model PerformanceЧтениеImproving Model PerformanceЧтениеSaving Association Rules to a File or DataFrameЧтениеExploring Patterns in DataЗадание
09Finding Groups of Data Clustering with k-means11 материалов

Lesson 1

Finding Groups of Data Clustering with k-means - Overview VideoВидеоIntroductionЧтениеClusters of Clustering AlgorithmsЧтениеThe K-Means Clustering AlgorithmЧтениеChoosing the Appropriate Number of ClustersЧтениеCollecting DataЧтениеData Preparation Dummy Coding Missing ValuesЧтениеTraining a Model on the DataЧтениеEvaluating Model PerformanceЧтениеImproving Model PerformanceЧтениеExploring Data Grouping and StandardizationЗадание
10Evaluating Model Performance13 материалов

Lesson 1

Evaluating Model Performance - Overview VideoВидеоIntroductionЧтениеA Closer Look at Confusion MatricesЧтениеBeyond Accuracy Other Measures of PerformanceЧтениеThe Matthews Correlation CoefficientЧтениеSensitivity and SpecificityЧтениеThe F-MeasureЧтениеComparing ROC CurvesЧтениеThe Area Under the ROC CurveЧтениеEstimating Future PerformanceЧтениеCross-ValidationЧтениеBootstrap SamplingЧтениеEvaluating Model Performance FundamentalsЗадание
11Being Successful with Machine Learning13 материалов

Lesson 1

Being Successful with Machine Learning - Overview VideoВидеоIntroductionЧтениеWhat Makes a Successful Machine Learning ModelЧтениеAvoiding Obvious PredictionsЧтениеConducting Fair EvaluationsЧтениеConsidering Real-World ImpactsЧтениеBuilding Trust in the ModelЧтениеPutting the Science in Data ScienceЧтениеUsing R Notebooks and R MarkdownЧтениеPerforming Advanced Data ExplorationЧтениеEncountering Outliers A Real-World PitfallЧтениеExample Using ggplot2 for Visual Data ExplorationЧтениеMastering Machine Learning FundamentalsЗадание
12Advanced Data Preparation15 материалов

Lesson 1

Advanced Data Preparation - Overview VideoВидеоIntroductionЧтениеThe Impact of Big Data and Deep LearningЧтениеFeature Engineering in PracticeЧтениеHint 2 Find Insights Hidden in TextЧтениеTransform Numeric RangesЧтениеPractice: Designing Features for a Churn ModelDIALOGUEUtilize Related RowsЧтениеAppend External DataЧтениеExploring R's TidyverseЧтениеReading Rectangular Files Faster with readr and readxlЧтениеPreparing and Piping Data with dplyrЧтениеTransforming Text with stringrЧтениеCleaning Dates with lubridateЧтениеMastering Data Preparation in Machine LearningЗадание
13Challenging Data: Too Much, Too Little, Too Complex20 материалов

Lesson 1

Challenging Data: Too Much, Too Little, Too Complex - Overview VideoВидеоIntroductionЧтениеFeature SelectionЧтениеWrapper Methods and Embedded MethodsЧтениеExample Using Stepwise Regression for Feature SelectionЧтениеExample Using Boruta for Feature SelectionЧтениеUnderstanding Principal Component AnalysisЧтениеExample Using PCA to Reduce Highly Dimensional Social Media DataЧтениеPractice: Choosing Your Dimensionality Reduction StrategyDIALOGUEMaking Use of Sparse DataЧтениеExample Remapping Sparse Categorical DataЧтениеExample Binning Sparse Numeric DataЧтениеHandling Missing DataЧтениеPerforming Missing Value ImputationЧтениеMissing Value PatternsЧтениеThe Problem of Imbalanced DataЧтениеGenerating a Synthetic Balanced Dataset with SMOTEЧтениеExample Applying the SMOTE Algorithm in RЧтениеConsidering Whether Balanced Is Always BetterЧтениеNavigating Data Complexity in Machine LearningЗадание
14Building Better Learners16 материалов

Lesson 1

Building Better Learners - Overview VideoВидеоIntroductionЧтениеDetermining the Scope of Hyperparameter TuningЧтениеExample Using caret for Automated TuningЧтениеCreating a Simple Tuned ModelЧтениеCustomizing the Tuning ProcessЧтениеReflect: Balancing Model Performance and CostDIALOGUEImproving Model Performance with EnsemblesЧтениеPopular Ensemble-Based AlgorithmsЧтениеBoostingЧтениеRandom ForestsЧтениеGradient BoostingЧтениеExtreme Gradient Boosting with XGBoostЧтениеWhy Are Tree-Based Ensembles So Popular?ЧтениеPractical Methods for Blending and Stacking in RЧтениеMastering Ensemble Methods and Model OptimizationЗадание
15Making Use of Big Data19 материалов

Lesson 1

Making Use of Big Data - Overview VideoВидеоIntroductionЧтениеChoosing Appropriate Tasks for Deep LearningЧтениеThe TensorFlow and Keras Deep Learning FrameworksЧтениеUnderstanding Convolutional Neural NetworksЧтениеTransfer Learning and Fine TuningЧтениеUnsupervised Learning and Big DataЧтениеUnderstanding Word EmbeddingsЧтениеExample Using word2vec for Understanding Text in RЧтениеVisualizing Highly Dimensional DataЧтениеUnderstanding the t-SNE AlgorithmЧтениеExample Visualizing Data's Natural Clusters With t-SNEЧтениеPractice: Interpret Visualizations to Guide ModelingDIALOGUEAdapting R to Handle Large DatasetsЧтениеUsing a Database Backend for dplyr with dbplyrЧтениеEnabling Parallel Processing in RЧтениеParallel Computing with MapReduce Concepts via Apache SparkЧтениеLearning via Distributed and Scalable Algorithms with H2OЧтениеExploring Deep Learning and Data Analysis MethodsЗадание