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Core Machine Learning & Evaluation · LearnSpace
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Core Machine Learning & Evaluation

Курс от Packt
Средний≈ 12.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will build a strong foundation in machine learning and model evaluation techniques. You will begin by learning the core concepts of machine learning, including supervised learning, regression models, and classification techniques. The course will then guide you through more advanced topics like feature engineering, model evaluation methods, and hyperparameter tuning, which are essential for building high-performing machine learning models. By working through hands-on projects, you'll apply these concepts and tools in real-world scenarios. Throughout the course, you will explore key machine learning algorithms such as decision trees, random forests, boosting, and ensemble learning methods. You'll also learn how to evaluate and optimize models using techniques like cross-validation and hyperparameter tuning. These skills will enable you to refine your models and improve their accuracy, ensuring that they are ready for real-world applications. This course is suitable for anyone looking to deepen their understanding of machine learning, model evaluation, and optimization. While there are no strict prerequisites, a basic understanding of Python programming and machine learning concepts is recommended. The course is designed for intermediate learners, and the content will provide valuable skills for anyone looking to pursue a career in data science or machine learning engineering. By the end of the course, you will be able to implement and optimize machine learning models using various algorithms, perform feature engineering and selection, evaluate models using cross-validation, and apply advanced techniques such as boosting and ensemble methods.

Навыки, которые вы освоите

Performance TuningModel EvaluationModel OptimizationFeature EngineeringSupervised LearningRandom Forest AlgorithmMachine Learning MethodsMachine Learning AlgorithmsRegression AnalysisMachine LearningModel TrainingApplied Machine LearningClassification Algorithms

Программа курса

4 модулей · 45 учебных материалов

01Week 5: Introduction to Machine Learning12 материалов

Week 5: Introduction to Machine Learning

Introduction to the Course 'Core Machine Learning & Evaluation'ЧтениеFull Specialization ResourcesЧтениеIntroduction to Week 5 Introduction to Machine LearningВидеоDay 1: Machine Learning Basics and TerminologyВидео

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Packt - Course Instructors

Преподаватель курса

Core Machine Learning & Evaluation
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 12.4 ч

4 модулей

Язык: Английский

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

Часть программы вашего университета
Day 2: Introduction to Supervised Learning and Regression ModelsВидео
Day 3: Advanced Regression Models – Polynomial Regression and RegularizationВидео
Day 4: Introduction to Classification and Logistic RegressionВидео
Day 5: Model Evaluation and Cross-ValidationВидео
Day 6: k-Nearest Neighbors (k-NN) AlgorithmВидео
Day 7: Supervised Learning Mini ProjectВидео
Machine Learning Fundamentals: Features, Targets, and Data SplittingDIALOGUE
Week 5: Introduction to Machine Learning - AssessmentЗадание
02Week 6: Feature Engineering and Model Evaluation10 материалов

Week 6: Feature Engineering and Model Evaluation

Introduction to Week 6 Feature Engineering and Model EvaluationВидеоDay 1: Introduction to Feature EngineeringВидеоDay 2: Data Scaling and NormalizationВидеоDay 3: Encoding Categorical VariablesВидеоDay 4: Feature Selection TechniquesВидеоDay 5: Creating and Transforming FeaturesВидеоDay 6: Model Evaluation TechniquesВидеоDay 7: Cross-Validation and Hyperparameter TuningВидеоApplying Feature Engineering and Model Evaluation TechniquesDIALOGUEWeek 6: Feature Engineering and Model Evaluation - AssessmentЗадание
03Week 7: Advanced Machine Learning Algorithms10 материалов

Week 7: Advanced Machine Learning Algorithms

Introduction to Week 7 Advanced Machine Learning AlgorithmsВидеоDay 1: Introduction to Ensemble LearningВидеоDay 2: Bagging and Random ForestsВидеоDay 3: Boosting and Gradient BoostingВидеоDay 4: Introduction to XGBoostВидеоDay 5: LightGBM and CatBoostВидеоDay 6: Handling Imbalanced DataВидеоDay 7: Ensemble Learning Project – Comparing Models on a Real DatasetВидеоEnsemble Learning on Real DataDIALOGUEWeek 7: Advanced Machine Learning Algorithms - AssessmentЗадание
04Week 8: Model Tuning and Optimization13 материалов

Week 8: Model Tuning and Optimization

Introduction to Week 8 Model Tuning and OptimizationВидеоDay 1: Introduction to Hyperparameter TuningВидеоDay 2: Grid Search and Random SearchВидеоDay 3: Advanced Hyperparameter Tuning with Bayesian OptimizationВидеоDay 4: Regularization Techniques for Model OptimizationВидеоDay 5: Cross-Validation and Model Evaluation TechniquesВидеоDay 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCVВидеоDay 7: Optimization Project – Building and Tuning a Final ModelВидеоConclusion to the Course 'Core Machine Learning & Evaluation'ЧтениеHyperparameter Tuning and Model OptimizationDIALOGUEWeek 8: Model Tuning and Optimization - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание