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.
AI and Machine Learning Algorithms and Techniques
通过 Coursera Plus 提高技能，仅需 239 美元/年（原价 399 美元）。立即节省
AI and Machine Learning Algorithms and Techniques
本课程是 Microsoft AI & ML Engineering 专业证书 的一部分
位教师： Microsoft
11,367 人已注册
包含在 中
•
了解更多
深入了解一个主题并学习基础知识。
4.7
64 条评论
中级 等级
推荐体验
推荐体验
中级
You should have completed the first course in the program, or have equivalent experience with the concepts taught in those courses.
灵活的计划
5 周 在 10 小时 一周
自行安排学习进度
98%
大多数学生喜欢此课程
深入了解一个主题并学习基础知识。
4.7
64 条评论
中级 等级
推荐体验
推荐体验
中级
You should have completed the first course in the program, or have equivalent experience with the concepts taught in those courses.
灵活的计划
5 周 在 10 小时 一周
自行安排学习进度
98%
大多数学生喜欢此课程
您将获得的技能
- Applied Machine Learning
- LLM Application
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Machine Learning Algorithms
- Deep Learning
- Large Language Modeling
- Feature Engineering
- Model Evaluation
- Dimensionality Reduction
- Artificial Intelligence and Machine Learning (AI/ML)
- Predictive Modeling
- Data Preprocessing
您将学习的工具
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累 Software Development 领域的专业知识
本课程是 Microsoft AI & ML Engineering 专业证书 专项课程的一部分
在注册此课程时，您还会同时注册此专业证书。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 Microsoft 获得可共享的职业证书
该课程共有5个模块
In this module, you'll embark on a comprehensive journey through the essentials of supervised ML. This module is designed to equip you with a robust understanding and practical skills in the field, ensuring you're well prepared to tackle real-world data problems. By the end of this module, you'll not only have a strong theoretical foundation but also practical experience in supervised learning, enabling you to confidently develop, evaluate, and optimize predictive models for a variety of applications.
涵盖的内容
9个视频30篇阅读材料15个作业
9个视频•总计45分钟
- Introduction to the AI/ML engineering advanced professional certificate program•4分钟
- Introduction to the AI/ML algorithms and techniques course•5分钟
- The importance of algorithms and techniques in your work•8分钟
- What is supervised learning?•7分钟
- Compare implementation techniques using Python•5分钟
- Use case demonstration of evaluation metrics•5分钟
- Use case demonstration of cross-validation and multiple metrics in ML•4分钟
- Walkthrough: Use cases of feature selection techniques in live demonstrations (Optional)•4分钟
- Summary: Supervised learning•5分钟
30篇阅读材料•总计576分钟
- Welcome to the Coursera Community•2分钟
- Microsoft updates•2分钟
- Practice activity: Setting up your environment in Microsoft Azure•30分钟
- Walkthrough: Setting up your environment in Microsoft Azure (Optional)•0分钟
- Practice activity: Creating your own code repository using Git•45分钟
- Walkthrough: Creating your own code repository using Git (Optional)•0分钟
- Course syllabus: AI and Machine Learning Algorithms and Techniques •10分钟
- Key principles and approaches to supervised learning•10分钟
- Best practices for implementing supervised learning algorithms•10分钟
- Practice activity: Integrating linear regression•30分钟
- Walkthrough: Integrating linear regression (Optional)•0分钟
- Practice activity: Implementing logistic regression•30分钟
- Walkthrough: Implementing logistic regression (Optional)•0分钟
- Practice activity: Implementing decision trees•30分钟
- Walkthrough: Implementing decision trees (Optional)•0分钟
- Practice activity: Implementing and comparing models•75分钟
- Walkthrough: Implementing and comparing models (Optional)•0分钟
- Evaluation metrics for supervised learning models•12分钟
- Practice activity: Applying metrics and cross-validation•90分钟
- Walkthrough: Applying metrics and cross-validation (Optional)•0分钟
- Feature selection methods: Backward elimination, forward selection, and LASSO•5分钟
- Practice activity: Implementing backward elimination•40分钟
- Walkthrough: Implementing backward elimination (Optional)•0分钟
- Practice activity: Implementing forward selection•40分钟
- Walkthrough: Implementing forward selection (Optional)•0分钟
- Practice activity: Implementing LASSO•45分钟
- Walkthrough: Implementing LASSO (Optional)•0分钟
- Practice activity: Implementing feature selection techniques on a given dataset •60分钟
- Walkthrough: Implementing feature selection techniques on a given dataset (Optional)•0分钟
- Industry exemplar: Feature selection techniques•10分钟
15个作业•总计93分钟
- Graded quiz: Feature selection techniques•30分钟
- Knowledge check: Algorithms and techniques•10分钟
- Reflection: Setting up your environment in Microsoft Azure•3分钟
- Reflection: Creating your own code repository•3分钟
- Knowledge check: Supervised learning•10分钟
- Reflection: Integrating linear regression•3分钟
- Reflection: Implementing logistic regression•3分钟
- Reflection: Implementing decision trees•3分钟
- Reflection: Implementing and comparing models•3分钟
- Reflection: Applying metrics and cross-validation•3分钟
- Knowledge check: Cross-validation and multiple metrics•10分钟
- Reflection: Implementing backward elimination•3分钟
- Reflection: Implementing forward selection•3分钟
- Reflection: Implementing LASSO•3分钟
- Reflection: Implementing feature selection techniques on a given dataset•3分钟
This module is a deep dive into the world of data analysis where the patterns and insights are uncovered without predefined labels. It is tailored to provide a comprehensive understanding and practical skills in unsupervised learning, empowering you to discover hidden structures within your data. By the end of this module, you'll have a solid grasp of unsupervised learning concepts and practical skills in implementing, analyzing, and comparing different algorithms. This knowledge will enable you to unlock valuable insights from complex datasets and make informed decisions based on your analyses.
涵盖的内容
4个视频18篇阅读材料9个作业
4个视频•总计19分钟
- Overview of unsupervised learning•3分钟
- How to implement and visualize clustering•5分钟
- Use case demonstration of dimensionality reduction•5分钟
- Walkthrough: Implementing unsupervised learning methods (Optional)•6分钟
18篇阅读材料•总计355分钟
- Key principles and approaches to unsupervised learning•10分钟
- Introduction to clustering techniques•10分钟
- Practice activity: Implementing k-means clustering•40分钟
- Walkthrough: Implementing k-means clustering (Optional)•0分钟
- Practice activity: Implementing DBSCAN clustering•30分钟
- Walkthrough: Implementing DBSCAN clustering (Optional)•0分钟
- Practice activity: Implementing clustering and visualization•45分钟
- Walkthrough: Clustering and visualization (Optional)•0分钟
- Dimensionality reduction techniques•10分钟
- Practice activity: Implementing dimensionality reduction techniques•60分钟
- Walkthrough: Implementing dimensionality reduction techniques (Optional)•0分钟
- Comparing unsupervised learning approaches for different datasets•10分钟
- Practice activity: Interpreting clustering and dimensionality reduction outcomes•45分钟
- Walkthrough: Interpreting clustering and dimensionality reduction outcomes (Optional)•0分钟
- Discussion: Comparing unsupervised learning approaches for different datasets•30分钟
- Summary: Unsupervised learning•10分钟
- Practice activity: Implementing unsupervised learning methods•50分钟
- Industry exemplar: Application of unsupervised learning techniques•5分钟
9个作业•总计78分钟
- Graded quiz: Unsupervised learning•30分钟
- Knowledge check: Unsupervised learning principles•15分钟
- Reflection: Implementing k-means clustering•3分钟
- Reflection: Implementing DBSCAN clustering•3分钟
- Reflection: Implementing clustering and visualization•3分钟
- Reflection: Implementing dimensionality reduction techniques•3分钟
- Knowledge check: Dimensionality reduction•15分钟
- Reflection: Interpreting clustering and dimensionality reduction outcomes•3分钟
- Reflection: Implementing unsupervised learning methods•3分钟
This module is designed to provide an in-depth exploration of cutting-edge techniques in ML. This module merges foundational reinforcement learning concepts with advanced strategies for enhancing language generation models, offering a well-rounded understanding of these pivotal areas in AI. By the end of this module, you’ll be equipped with theoretical knowledge and practical experience in reinforcement learning and language model enhancement. This comprehensive understanding will enable you to tackle complex problems and contribute to innovative solutions in the rapidly evolving field of AI.
涵盖的内容
6个视频11篇阅读材料6个作业
6个视频•总计33分钟
- Overview of reinforcement learning•5分钟
- Comparing implementation techniques using Python•6分钟
- Use case demonstration for applying model evaluation metrics•7分钟
- Summary of reinforcement learning and other approaches•5分钟
- Walkthrough: Reinforcement learning and other approaches (Optional)•5分钟
- Industry exemplar: Reinforcement learning and other approaches•5分钟
11篇阅读材料•总计375分钟
- Key principles and approaches of reinforcement learning•20分钟
- Practice activity: Comparing and reinforcing learning algorithms•90分钟
- Walkthrough: Comparing Q-learning and policy gradients (Optional)•0分钟
- Evaluation metrics for reinforcement learning models•15分钟
- Practice activity: Applying model evaluation metrics in reinforcement learning•90分钟
- Walkthrough: Applying model evaluation metrics (Optional)•0分钟
- Comparing reinforcement learning with supervised and unsupervised learning•10分钟
- Use case demonstration for supervised, unsupervised, and reinforcement learning•20分钟
- Discussion: Comparative analysis of learning paradigms•30分钟
- Use case comparison of supervised, unsupervised, and reinforcement learning•10分钟
- Practice activity: Implementing reinforcement learning and other approaches•90分钟
6个作业•总计69分钟
- Graded quiz: Reinforcement learning and other approaches•30分钟
- Knowledge check: Reinforcement learning principles•15分钟
- Reflection: Q-Learning and Policy Gradients•3分钟
- Reflection: Model evaluation metrics•3分钟
- Knowledge check: Evaluation metrics for performance models•15分钟
- Reflection: Implemented supervised learning, unsupervised learning, and reinforcement learning approaches•3分钟
This module is designed to provide a comprehensive introduction to neural networks and their applications in modern AI. It will guide you through the core principles of deep learning, from basic neural network architecture to advanced applications in image and text data, while also exploring the significance of deep learning within the realm of generative AI (GenAI). By the end of this module, you will have a solid grasp of neural network architectures, practical experience with deep learning techniques, and a clear understanding of how these technologies are applied within the broader landscape of GenAI. This knowledge will enable you to leverage deep learning effectively in academic and real-world scenarios.
涵盖的内容
5个视频14篇阅读材料8个作业
5个视频•总计32分钟
- Overview of neural networks•5分钟
- Walkthrough: Implementing and comparing neural network architectures in TensorFlow and PyTorch (Optional)•9分钟
- Use case demonstration of FNNs, CNNs, and RNNs•5分钟
- Walkthrough: Analyzing a dataset and implementing a neural network for deep learning analysis (Optional)•6分钟
- Hear from an expert: Industry exemplar of deep learning and neural networks•7分钟
14篇阅读材料•总计485分钟
- Key features and architectures of neural networks•10分钟
- Implementing neural networks in Azure•15分钟
- Comparing neural network implementation techniques using Python•15分钟
- Practice activity: Implementing a neural network with TensorFlow•75分钟
- Walkthrough: Implementing a neural network with TensorFlow (Optional)•0分钟
- Practice activity: Implementing and comparing neural network architectures•90分钟
- Explanation of deep learning techniques•15分钟
- Practice activity: Implementing deep learning techniques•90分钟
- Walkthrough: Implementing deep learning techniques (FNN, CNN, RNN) (Optional)•0分钟
- Implementation of deep learning techniques: GANs and autoencoders•15分钟
- Practice activity: Evaluating deep learning models in the context of generative AI•105分钟
- Walkthrough: Evaluating deep learning models in the context of generative AI (Optional)•0分钟
- Summary: Deep learning and neural networks•10分钟
- Practice activity: Analyzing a dataset and implementing a neural network for deep learning analysis•45分钟
8个作业•总计75分钟
- Graded quiz: Deep learning and neural networks•30分钟
- Knowledge check: Key architectures and features of neural networks•15分钟
- Reflection: Implementing a neural network with TensorFlow•3分钟
- Reflection: Implementing and comparing neural network architectures•3分钟
- Reflection: Implementing deep learning techniques•3分钟
- Knowledge check: Deep learning techniques•15分钟
- Reflection: Evaluating deep learning models in the context of generative AI•3分钟
- Reflection: Analyzing a dataset and implementing a neural network for deep learning analysis•3分钟
This module is a focused exploration of the roles, responsibilities, and approaches in the field of AI and ML within a business environment. It is designed to provide a comprehensive understanding of how AI/ML engineers operate, the distinctions between handling in-house developed models versus pretrained models and how they collaborate with other key roles in the corporate ecosystem. By the end of this module, you will have a clear understanding of the various approaches to AI/ML engineering, the specific responsibilities associated with different types of models, and the collaborative dynamics within a corporate setting. This knowledge will empower you to navigate and contribute effectively to AI/ML projects in a business environment.
涵盖的内容
7个视频16篇阅读材料7个作业1次同伴评审
7个视频•总计33分钟
- Overview of AI/ML engineering approaches•6分钟
- Hear from an expert: Aligning AI with organizational goals•4分钟
- The Importance of collaboration in AI/ML professions•4分钟
- Hear from an expert: Balancing business and technical priorities•7分钟
- Summary: AI/ML engineering and working with models•6分钟
- Summary, thank you, and good luck•3分钟
- Thank you, and congratulations!•2分钟
16篇阅读材料•总计355分钟
- Real-world case studies of corporate AI/ML implementations•10分钟
- Practice activity: Implementing a corporate approach in context•30分钟
- Practice activity: Deploying and repairing AI/ML systems•30分钟
- Walkthrough: Deploying and repairing AI/ML systems (Optional)•0分钟
- The roles of AI/ML engineers•10分钟
- Detailed role descriptions of AI/ML engineers in industry•10分钟
- Discussion: Comparing AI/ML engineer roles•30分钟
- Considering your career in AI/ML engineering•10分钟
- Practice activity: Identifying your strengths, weaknesses, and interests in AI/ML engineering•30分钟
- Understanding team dynamics in AI/ML development teams•10分钟
- Comprehensive guide•5分钟
- Tools and platforms for further learning•5分钟
- Industry exemplar: Discussing roles in AI/ML•10分钟
- Practice activity: Creating an AI/ML development plan for a fictitious project•45分钟
- Walkthrough: Creating an AI/ML development plan for customer churn prediction (Optional)•0分钟
- Practice activity: Designing and developing an AI/ML solution•120分钟
7个作业•总计72分钟
- Graded quiz: AI/ML engineering and working with models•30分钟
- Reflection: Deploying and repairing AI/ML systems•3分钟
- Knowledge check: AI/ML engineering approaches•15分钟
- Knowledge check: Matching AI/ML engineering roles to responsibilities•15分钟
- Reflection: Identifying your strengths, weaknesses, and interests in AI/ML engineering•3分钟
- Reflection: Creating an AI/ML development plan for a fictitious project•3分钟
- Knowledge check: Designing and developing an AI/ML solution•3分钟
1次同伴评审•总计45分钟
- Course assignment: Producing a comprehensive AI/ML project technical report•45分钟
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
位教师
授课教师评分
4.8 (15个评价)授课教师评分
我们要求所有学生根据授课教师的教学风格和质量提供对授课教师的反馈。
287 门课程•2,427,538 名学生
提供方
提供方
Microsoft
Our goal at Microsoft is to empower every individual and organization on the planet to achieve more. In this next revolution of digital transformation, growth is being driven by technology. Our integrated cloud approach creates an unmatched platform for digital transformation. We address the real-world needs of customers by seamlessly integrating Microsoft 365, Dynamics 365, LinkedIn, GitHub, Microsoft Power Platform, and Azure to unlock business value for every organization—from large enterprises to family-run businesses. The backbone and foundation of this is Azure.
从 Software Development 浏览更多内容
状态：免费试用免费试用
状态：免费试用免费试用FFractal Analytics
课程
状态：免费试用免费试用
状态：免费试用免费试用CCoursera
课程
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
学生评论
4.7
64 条评论
- 5 stars
81.25%
- 4 stars
12.50%
- 3 stars
0%
- 2 stars
3.12%
- 1 star
3.12%
显示 3/64 个
K
KA
5·
已于 Mar 31, 2025审阅
It was very well tailored for all types of learners.
常见问题
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.
You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware. Note: the free trial version of Azure is time limited and may expire before completion of the program.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。