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Applied Machine Learning and Model Optimization · LearnSpace
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Applied Machine Learning and Model Optimization

Курс от Packt
Продвинутый≈ 22.7 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. This course dives deep into applied machine learning and model optimization, covering everything from foundational concepts to advanced algorithms. You'll gain hands-on experience working with different types of machine learning models, evaluating their performance, and fine-tuning them for optimal results. The course emphasizes practical, real-world applications, with interactive projects and mini-projects to ensure you can implement what you learn. Throughout the course, you'll explore core machine learning algorithms such as regression, classification, ensemble methods, and advanced techniques like XGBoost and LightGBM. You'll also focus on model optimization, including hyperparameter tuning, cross-validation, and regularization techniques. These skills will allow you to enhance the performance of your models, even in complex scenarios. This course is designed for learners who already have a basic understanding of machine learning and wish to build more advanced skills in model building and optimization. It is ideal for those looking to pursue careers in data science, machine learning engineering, or AI development. By the end of the course, you will be able to implement various machine learning algorithms, optimize model performance using hyperparameter tuning, and evaluate models effectively for real-world tasks.

Навыки, которые вы освоите

Supervised LearningUnsupervised LearningModel OptimizationModel EvaluationApplied Machine LearningMachine Learning AlgorithmsFeature EngineeringFine-tuningData PreprocessingPredictive ModelingModel TrainingData TransformationClassification AlgorithmsDimensionality Reduction

Программа курса

7 модулей · 99 учебных материалов

01Introduction to Machine Learning12 материалов

Week 5: Introduction to Machine Learning

Introduction to the Course 'Applied Machine Learning and Model Optimization'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

Applied Machine Learning and Model Optimization
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 22.7 ч

7 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 Basics: Features, Targets, and Data SplitsDIALOGUE
Introduction to Machine Learning - AssessmentЗадание
02Feature 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 TuningВидеоFeature Engineering Essentials: Identifying and Planning Feature StrategiesDIALOGUEFeature Engineering and Model Evaluation - AssessmentЗадание
03Advanced 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 DatasetВидеоComparing Ensemble Learning Techniques on Imbalanced DataDIALOGUEAdvanced Machine Learning Algorithms - AssessmentЗадание
04Model Tuning and Optimization10 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 ModelВидеоComparing Grid Search and Random Search for Hyperparameter TuningDIALOGUEModel Tuning and Optimization - AssessmentЗадание
05Intermediate Projects13 материалов

Days 50–60: Intermediate Projects

Day 50: Weather Dashboard AppВидеоDay 51: Expense TrackerВидеоDay 52: File Organizer ToolВидеоDay 53: Tic-Tac-Toe GameВидеоDay 54: Mini ChatbotВидеоDay 55: Music Playlist OrganizerВидеоDay 56: Personal Budget PlannerВидеоDay 57: ASCII Art GeneratorВидеоDay 58: Pomodoro TimerВидеоDay 59: Markdown to HTML ConverterВидеоDay 60: Personal Diary AppВидеоBuilding a Mini Chatbot with Keyword ResponsesDIALOGUEIntermediate Projects - AssessmentЗадание
06Advanced Intermediate Projects12 материалов

Days 61–70: Advanced Intermediate Projects

Day 61: Social Media ScraperВидеоDay 62: Automated Backup ToolВидеоDay 63: Movie Recommendation SystemВидеоDay 64: PDF Merger ToolВидеоDay 65: Portfolio Website BackendВидеоDay 66: Flashcards Learning AppВидеоDay 67: Stock Market DashboardВидеоDay 68: Task SchedulerВидеоDay 69: Currency ConverterВидеоDay 70: Data Visualizer AppВидеоBuilding a Social Media Scraper with BeautifulSoupDIALOGUEAdvanced Intermediate Projects - AssessmentЗадание
07Machine Learning Algorithms and Implementation in Python32 материалов

Machine Learning Algorithms and Implementation in Python

Introduction to Machine Learning Algorithms and Implementation in PythonВидео1. Supervised Learning Algorithms: Linear Regression ImplementationВидео2. Supervised Learning Algorithms: Ridge and Lasso Regression ImplementationВидео3. Supervised Learning Algorithms: Polynomial Regression ImplementationВидео4. Supervised Learning Algorithms: Logistic Regression ImplementationВидео5. Supervised Learning Algorithms: K-Nearest Neighbors (KNN) ImplementationВидео6. Supervised Learning Algorithms: Support Vector Machines (SVM) ImplementationВидео7. Supervised Learning Algorithms: Decision Trees ImplementationВидео8. Supervised Learning Algorithms: Random Forests ImplementationВидео9. Supervised Learning Algorithms: Gradient Boosting ImplementationВидео10. Supervised Learning Algorithms: Naive Bayes ImplementationВидео11. Unsupervised Learning Algorithms: K-Means Clustering ImplementationВидео12. Unsupervised Learning Algorithms: Hierarchical Clustering ImplementationВидео13. Unsupervised Learning Algorithms: DBSCANВидео14. Unsupervised Learning Algorithms: Gaussian Mixture Models (GMM)Видео15. Unsupervised Learning Algorithms: Principal Component Analysis (PCA)Видео16. Unsupervised Learning Algorithms: t-Distributed Stochastic Neighbor EmbeddingВидео17. Unsupervised Learning Algorithms: Autoencoders ImplementationВидео18. Self-Training ImplementationВидео19. Q-Learning ImplementationВидео20. Deep Q-Networks (DQN) ImplementationВидео21. Policy Gradient Methods ImplementationВидео22. One-Class SVM ImplementationВидео23. Isolation Forest ImplementationВидео24. Convolutional Neural Networks (CNNs) ImplementationВидео25. Recurrent Neural Networks (RNNs) ImplementationВидео26. Long Short-Term Memory (LSTM) ImplementationВидео27. Transformers ImplementationВидеоConclusion to the Course 'Applied Machine Learning and Model Optimization'ЧтениеMachine Learning Algorithms and Implementation in Python - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание