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AI and Machine Learning Algorithms and Techniques · LearnSpace
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courseraПрограммирование

AI and Machine Learning Algorithms and Techniques

Курс от Microsoft
Средний≈ 45.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course covers the core algorithms and techniques used in AI and ML, including approaches that use pre-trained large-language models (LLMs). You will explore supervised, unsupervised, and reinforcement learning paradigms, as well as deep learning approaches, including how these operate in pre-trained LLMs. The course emphasizes the practical application of these techniques and their strengths and limitations in solving different types of business problems. By the end of this course, you will be able to: 1. Implement, evaluate, and explain supervised, unsupervised, and reinforcement learning algorithms. 2. Apply feature selection and engineering techniques to improve model performance. 3. Describe deep learning models for complex AI tasks. 4. Assess the suitability of various AI & ML techniques for specific business problems. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.

Навыки, которые вы освоите

Unsupervised LearningFeature EngineeringApplied Machine LearningArtificial Neural NetworksDimensionality ReductionModel OptimizationSupervised LearningArtificial Intelligence and Machine Learning (AI/ML)Reinforcement LearningDeep LearningModel TrainingUnstructured DataLarge Language ModelingGenerative Model ArchitecturesLLM ApplicationStatistical Machine LearningMachine Learning AlgorithmsGenerative AIModel Evaluation

Программа курса

5 модулей · 166 учебных материалов

01Supervised learning54 материалов

Welcome to the course

Introduction to the AI/ML engineering advanced professional certificate programВидеоIntroduction to the AI/ML algorithms and techniques courseВидеоThe importance of algorithms and techniques in your workВидеоKnowledge check: Algorithms and techniquesЗадание

Учитесь у экспертов

Microsoft

Преподаватель курса

AI and Machine Learning Algorithms and Techniques
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 45.7 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Пушту, Испанский, Дари, Японский, Казахский, Венгерский

Часть программы вашего университета
Welcome to the Coursera CommunityЧтение
Microsoft updatesЧтение
Practice activity: Setting up your environment in Microsoft AzureЧтение
Reflection: Setting up your environment in Microsoft AzureЗадание
Walkthrough: Setting up your environment in Microsoft Azure (Optional)Чтение
Practice activity: Creating your own code repository using GitЧтение
Reflection: Creating your own code repositoryЗадание
Walkthrough: Creating your own code repository using Git (Optional)Чтение
Course syllabus: AI and Machine Learning Algorithms and Techniques Чтение

Introduction to supervised learning

What is supervised learning?ВидеоKey principles and approaches to supervised learningЧтениеKnowledge check: Supervised learningЗадание

Implementing supervised learning algorithms

Best practices for implementing supervised learning algorithmsЧтениеPractice activity: Integrating linear regressionЧтениеReflection: Integrating linear regressionЗаданиеWalkthrough: Integrating linear regression (Optional)ЧтениеPractice activity: Implementing logistic regressionЧтениеReflection: Implementing logistic regressionЗаданиеWalkthrough: Implementing logistic regression (Optional)ЧтениеPractice activity: Implementing decision treesЧтениеReflection: Implementing decision treesЗаданиеWalkthrough: Implementing decision trees (Optional)ЧтениеCompare implementation techniques using PythonВидеоPractice activity: Implementing and comparing modelsЧтениеReflection: Implementing and comparing modelsЗаданиеWalkthrough: Implementing and comparing models (Optional)Чтение

Model evaluation metrics

Evaluation metrics for supervised learning modelsЧтениеUse case demonstration of evaluation metricsВидеоPractice activity: Applying metrics and cross-validationЧтениеReflection: Applying metrics and cross-validationЗаданиеWalkthrough: Applying metrics and cross-validation (Optional)ЧтениеUse case demonstration of cross-validation and multiple metrics in MLВидеоKnowledge check: Cross-validation and multiple metricsЗадание

Feature selection techniques

Feature selection methods: Backward elimination, forward selection, and LASSOЧтениеPractice activity: Implementing backward eliminationЧтениеReflection: Implementing backward eliminationЗаданиеWalkthrough: Implementing backward elimination (Optional)ЧтениеPractice activity: Implementing forward selectionЧтениеReflection: Implementing forward selectionЗаданиеWalkthrough: Implementing forward selection (Optional)ЧтениеPractice activity: Implementing LASSOЧтениеReflection: Implementing LASSOЗаданиеWalkthrough: Implementing LASSO (Optional)ЧтениеWalkthrough: Use cases of feature selection techniques in live demonstrations (Optional)Видео

Module summary: Supervised learning

Summary: Supervised learningВидеоPractice activity: Implementing feature selection techniques on a given dataset ЧтениеReflection: Implementing feature selection techniques on a given datasetЗаданиеWalkthrough: Implementing feature selection techniques on a given dataset (Optional)ЧтениеIndustry exemplar: Feature selection techniquesЧтениеGraded quiz: Feature selection techniquesЗадание
02Unsupervised learning31 материалов

Introduction to unsupervised learning

Overview of unsupervised learningВидеоKey principles and approaches to unsupervised learningЧтениеKnowledge check: Unsupervised learning principlesЗадание

Clustering techniques

Introduction to clustering techniquesЧтениеPractice activity: Implementing k-means clusteringЧтениеReflection: Implementing k-means clusteringЗаданиеWalkthrough: Implementing k-means clustering (Optional)ЧтениеPractice activity: Implementing DBSCAN clusteringЧтениеReflection: Implementing DBSCAN clusteringЗаданиеWalkthrough: Implementing DBSCAN clustering (Optional)ЧтениеHow to implement and visualize clusteringВидеоPractice activity: Implementing clustering and visualizationЧтениеReflection: Implementing clustering and visualizationЗаданиеWalkthrough: Clustering and visualization (Optional)Чтение

Dimensionality reduction

Dimensionality reduction techniquesЧтениеPractice activity: Implementing dimensionality reduction techniquesЧтениеReflection: Implementing dimensionality reduction techniquesЗаданиеWalkthrough: Implementing dimensionality reduction techniques (Optional)ЧтениеUse case demonstration of dimensionality reductionВидеоKnowledge check: Dimensionality reductionЗадание

Analyzing unsupervised learning results

Comparing unsupervised learning approaches for different datasetsЧтениеPractice activity: Interpreting clustering and dimensionality reduction outcomesЧтениеReflection: Interpreting clustering and dimensionality reduction outcomesЗаданиеWalkthrough: Interpreting clustering and dimensionality reduction outcomes (Optional)ЧтениеDiscussion: Comparing unsupervised learning approaches for different datasetsЧтение

Module summary: Unsupervised learning

Summary: Unsupervised learningЧтениеPractice activity: Implementing unsupervised learning methodsЧтениеReflection: Implementing unsupervised learning methodsЗаданиеWalkthrough: Implementing unsupervised learning methods (Optional)ВидеоIndustry exemplar: Application of unsupervised learning techniquesЧтениеGraded quiz: Unsupervised learningЗадание
03Reinforcement learning and other approaches23 материалов

Fundamentals of reinforcement learning

Overview of reinforcement learningВидеоKey principles and approaches of reinforcement learningЧтениеKnowledge check: Reinforcement learning principlesЗадание

Implementing reinforcement learning algorithms

Comparing implementation techniques using PythonВидеоPractice activity: Comparing and reinforcing learning algorithmsЧтениеReflection: Q-Learning and Policy GradientsЗаданиеWalkthrough: Comparing Q-learning and policy gradients (Optional)Чтение

Evaluating reinforcement learning models

Evaluation metrics for reinforcement learning modelsЧтениеUse case demonstration for applying model evaluation metricsВидеоPractice activity: Applying model evaluation metrics in reinforcement learningЧтениеReflection: Model evaluation metricsЗаданиеWalkthrough: Applying model evaluation metrics (Optional)ЧтениеKnowledge check: Evaluation metrics for performance modelsЗадание

Comparing ML paradigms

Comparing reinforcement learning with supervised and unsupervised learningЧтениеUse case demonstration for supervised, unsupervised, and reinforcement learningЧтениеDiscussion: Comparative analysis of learning paradigmsЧтениеUse case comparison of supervised, unsupervised, and reinforcement learningЧтение

Module summary: Reinforcement learning and other approaches

Summary of reinforcement learning and other approachesВидеоPractice activity: Implementing reinforcement learning and other approachesЧтениеReflection: Implemented supervised learning, unsupervised learning, and reinforcement learning approachesЗаданиеWalkthrough: Reinforcement learning and other approaches (Optional)ВидеоIndustry exemplar: Reinforcement learning and other approachesВидеоGraded quiz: Reinforcement learning and other approachesЗадание
04Deep learning and neural networks27 материалов

Overview of neural networks

Overview of neural networksВидеоKey features and architectures of neural networksЧтениеKnowledge check: Key architectures and features of neural networksЗадание

Implementing neural networks

Implementing neural networks in AzureЧтениеComparing neural network implementation techniques using PythonЧтениеPractice activity: Implementing a neural network with TensorFlowЧтениеReflection: Implementing a neural network with TensorFlowЗаданиеWalkthrough: Implementing a neural network with TensorFlow (Optional)ЧтениеPractice activity: Implementing and comparing neural network architecturesЧтениеReflection: Implementing and comparing neural network architecturesЗаданиеWalkthrough: Implementing and comparing neural network architectures in TensorFlow and PyTorch (Optional)Видео

Deep learning for image and text data

Explanation of deep learning techniquesЧтениеUse case demonstration of FNNs, CNNs, and RNNsВидеоPractice activity: Implementing deep learning techniquesЧтениеReflection: Implementing deep learning techniquesЗаданиеWalkthrough: Implementing deep learning techniques (FNN, CNN, RNN) (Optional)ЧтениеImplementation of deep learning techniques: GANs and autoencodersЧтение

Evaluating deep learning models

Practice activity: Evaluating deep learning models in the context of generative AIЧтениеReflection: Evaluating deep learning models in the context of generative AIЗаданиеWalkthrough: Evaluating deep learning models in the context of generative AI (Optional)Чтение

Module summary: Deep learning and neural networks

Summary: Deep learning and neural networksЧтениеPractice activity: Analyzing a dataset and implementing a neural network for deep learning analysisЧтениеReflection: Analyzing a dataset and implementing a neural network for deep learning analysisЗаданиеWalkthrough: Analyzing a dataset and implementing a neural network for deep learning analysis (Optional)ВидеоHear from an expert: Industry exemplar of deep learning and neural networksВидеоGraded quiz: Deep learning and neural networksЗадание
05The concepts in practice31 материалов

Corporate approaches to AI/ML engineering

Overview of AI/ML engineering approachesВидеоReal-world case studies of corporate AI/ML implementationsЧтениеHear from an expert: Aligning AI with organizational goalsВидеоPractice activity: Implementing a corporate approach in contextЧтениеPractice activity: Deploying and repairing AI/ML systemsЧтениеReflection: Deploying and repairing AI/ML systemsЗаданиеWalkthrough: Deploying and repairing AI/ML systems (Optional)ЧтениеKnowledge check: AI/ML engineering approachesЗадание

Responsibilities of AI/ML engineers

The roles of AI/ML engineersЧтениеDetailed role descriptions of AI/ML engineers in industryЧтениеDiscussion: Comparing AI/ML engineer rolesЧтениеKnowledge check: Matching AI/ML engineering roles to responsibilitiesЗаданиеConsidering your career in AI/ML engineeringЧтениеPractice activity: Identifying your strengths, weaknesses, and interests in AI/ML engineeringЧтение

Collaboration with other professionals

The Importance of collaboration in AI/ML professionsВидеоUnderstanding team dynamics in AI/ML development teamsЧтениеHear from an expert: Balancing business and technical prioritiesВидео

Review of additional reading and resources

Comprehensive guideЧтениеTools and platforms for further learningЧтение

Module summary: The concepts in practice

Summary: AI/ML engineering and working with modelsВидеоIndustry exemplar: Discussing roles in AI/MLЧтениеGraded quiz: AI/ML engineering and working with modelsЗаданиеPractice activity: Creating an AI/ML development plan for a fictitious projectЧтениеReflection: Creating an AI/ML development plan for a fictitious projectЗаданиеWalkthrough: Creating an AI/ML development plan for customer churn prediction (Optional)Чтение

Course summary: AI and Machine Learning Algorithms and Techniques

Summary, thank you, and good luckВидеоPractice activity: Designing and developing an AI/ML solutionЧтениеKnowledge check: Designing and developing an AI/ML solutionЗаданиеCourse assignment: Producing a comprehensive AI/ML project technical reportВзаимная проверкаThank you, and congratulations!Видео
Knowledge check: Deep learning techniquesЗадание
Reflection: Identifying your strengths, weaknesses, and interests in AI/ML engineeringЗадание