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Build Decision Trees, SVMs, and Artificial Neural Networks · LearnSpace
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Build Decision Trees, SVMs, and Artificial Neural Networks

Курс от CertNexus
Средний≈ 21.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job. This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

Навыки, которые вы освоите

Natural Language ProcessingArtificial Neural NetworksDecision Tree LearningSupervised LearningClassification And Regression Tree (CART)Machine Learning AlgorithmsConvolutional Neural NetworksRandom Forest AlgorithmComputer VisionRecurrent Neural Networks (RNNs)Predictive ModelingArtificial Intelligence and Machine Learning (AI/ML)Applied Machine LearningClassification AlgorithmsModel TrainingDeep Learning

Программа курса

5 модулей · 73 учебных материалов

01Build Decision Trees and Random Forests25 материалов

Overview

Build Decision Trees, SVMs, and Artificial Neural Networks Course IntroductionВидеоCAIP Specialization IntroductionВидеоBuild Decision Trees and Random Forests Module IntroductionВидеоOverviewЧтение

Учитесь у экспертов

Stacey McBrine

CDSP, CAIP, CIoTP, CIoTSP, CFR, CISSP, SSCP, CASP, CFR, CEI, CEH, ECSA, CHFI, CCNA, CCSI, CTT+, LINUX+, PENTEST+, SECURITY+, A+, SCNP, ITIL Foundations, ITIL SO, ITIL OSA, MCSA, MCITP

Build Decision Trees, SVMs, and Artificial Neural Networks
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 21.9 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Дари, Хинди, Японский, Казахский, Польский

Часть программы вашего университета
Get help and meet other learners. Join your Community!Чтение

Build Decision Tree Models

Decision TreeВидеоClassification and Regression Tree (CART)ВидеоGini Index ExampleВидеоCART HyperparametersВидеоPruningВидеоC4.5ВидеоBin DeterminationВидеоOne-Hot EncodingВидеоDecision Tree Algorithm ComparisonЧтениеDecision Trees Compared to Other AlgorithmsВидеоGuidelines for Building a Decision Tree ModelЧтениеBuilding a Decision Tree ModelЛабораторная

Build Random Forest Models

Ensemble LearningВидеоRandom ForestВидеоRandom Forest HyperparametersВидеоFeature Selection BenefitsВидеоGuidelines for Building a Random Forest ModelЧтениеBuilding a Random Forest ModelЛабораторная

Evaluate What You've Learned

Building Decision Trees and Random ForestsЗаданиеReflect on What You've LearnedОбсуждение
02Build Support-Vector Machines (SVM)15 материалов

Overview

Build Support-Vector Machines (SVM) Module IntroductionВидеоOverviewЧтение

Build SVM Models for Classification

Support-Vector Machines (SVMs)ВидеоSVMs for Linear ClassificationВидеоHard-Margin and Soft-Margin ClassificationВидеоSVMs for Non-Linear ClassificationВидеоKernel TrickВидеоKernel MethodsВидеоGuidelines for Building SVM Models for ClassificationЧтениеBuilding an SVM Model for ClassificationЛабораторная

Build SVM Models for Regression

SVMs for RegressionВидеоGuidelines for Building SVM Models for RegressionЧтениеBuilding an SVM Model for RegressionЛабораторная

Evaluate What You've Learned

Building SVMsЗаданиеReflect on What You've LearnedОбсуждение
03Build Multi-Layer Perceptrons (MLP)13 материалов

Overview

Build Multi-Layer Perceptrons (MLP) Module IntroductionВидеоOverviewЧтение

ANNs and MLPs

Artificial Neural Network (ANN)ВидеоPerceptronВидеоPerceptron TrainingВидеоMulti-Layer Perceptron (MLP)ВидеоANN LayersВидеоBackpropagationВидеоActivation FunctionsВидеоGuidelines for Building MLPsЧтениеBuilding an MLPЛабораторная

Evaluate What You've Learned

Building MLPsЗаданиеReflect on What You've LearnedОбсуждение
04Build Convolutional and Recurrent Neural Networks (CNN/RNN)18 материалов

Overview

Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module IntroductionВидеоOverviewЧтение

Build CNNs

Convolutional Neural Network (CNN)ВидеоCNN FiltersВидеоPadding and StrideВидеоCNN ArchitectureВидеоGenerative Adversarial Network (GAN)ВидеоGuidelines for Building CNNsЧтениеBuilding a CNNЛабораторная

Build RNNs

Recurrent Neural Network (RNN)ВидеоMemory CellВидеоRNN TrainingВидеоLong Short-Term Memory (LSTM) CellВидеоEmbeddingВидеоGuidelines for Building RNNsЧтениеBuilding an RNN

Evaluate What You've Learned

Building CNNs and RNNsЗаданиеReflect on What You've LearnedОбсуждение
05Apply What You've Learned2 материалов

Project

Course 4 ProjectЛабораторнаяBuilding a CNN to Classify Handwritten CharactersВзаимная проверка
Лабораторная