AI and Machine Learning Algorithms and Techniques
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
AI and Machine Learning Algorithms and Techniques
This course is part of Microsoft AI & ML Engineering Professional Certificate
Instructor: Microsoft
12,761 already enrolled
Included with
Ask Coursera
66 reviews
Recommended experience
66 reviews
Recommended experience
Skills you'll gain
- Model Optimization
- Deep Learning
- Artificial Neural Networks
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Algorithms
- LLM Application
- Applied Machine Learning
- Model Evaluation
- Large Language Modeling
- Feature Engineering
- Dimensionality Reduction
- Generative Model Architectures
- Statistical Machine Learning
- Supervised Learning
- Model Training
- Reinforcement Learning
- Unstructured Data
- Unsupervised Learning
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your Software Development expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Microsoft
There are 5 modules in this course
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.
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.
What's included
9 videos30 readings15 assignments
9 videos•Total 45 minutes
- Introduction to the AI/ML engineering advanced professional certificate program•4 minutes
- Introduction to the AI/ML algorithms and techniques course•5 minutes
- The importance of algorithms and techniques in your work•8 minutes
- What is supervised learning?•7 minutes
- Compare implementation techniques using Python•5 minutes
- Use case demonstration of evaluation metrics•5 minutes
- Use case demonstration of cross-validation and multiple metrics in ML•4 minutes
- Walkthrough: Use cases of feature selection techniques in live demonstrations (Optional)•4 minutes
- Summary: Supervised learning•5 minutes
30 readings•Total 576 minutes
- Welcome to the Coursera Community•2 minutes
- Microsoft updates•2 minutes
- Practice activity: Setting up your environment in Microsoft Azure•30 minutes
- Walkthrough: Setting up your environment in Microsoft Azure (Optional)•0 minutes
- Practice activity: Creating your own code repository using Git•45 minutes
- Walkthrough: Creating your own code repository using Git (Optional)•0 minutes
- Course syllabus: AI and Machine Learning Algorithms and Techniques •10 minutes
- Key principles and approaches to supervised learning•10 minutes
- Best practices for implementing supervised learning algorithms•10 minutes
- Practice activity: Integrating linear regression•30 minutes
- Walkthrough: Integrating linear regression (Optional)•0 minutes
- Practice activity: Implementing logistic regression•30 minutes
- Walkthrough: Implementing logistic regression (Optional)•0 minutes
- Practice activity: Implementing decision trees•30 minutes
- Walkthrough: Implementing decision trees (Optional)•0 minutes
- Practice activity: Implementing and comparing models•75 minutes
- Walkthrough: Implementing and comparing models (Optional)•0 minutes
- Evaluation metrics for supervised learning models•12 minutes
- Practice activity: Applying metrics and cross-validation•90 minutes
- Walkthrough: Applying metrics and cross-validation (Optional)•0 minutes
- Feature selection methods: Backward elimination, forward selection, and LASSO•5 minutes
- Practice activity: Implementing backward elimination•40 minutes
- Walkthrough: Implementing backward elimination (Optional)•0 minutes
- Practice activity: Implementing forward selection•40 minutes
- Walkthrough: Implementing forward selection (Optional)•0 minutes
- Practice activity: Implementing LASSO•45 minutes
- Walkthrough: Implementing LASSO (Optional)•0 minutes
- Practice activity: Implementing feature selection techniques on a given dataset •60 minutes
- Walkthrough: Implementing feature selection techniques on a given dataset (Optional)•0 minutes
- Industry exemplar: Feature selection techniques•10 minutes
15 assignments•Total 93 minutes
- Graded quiz: Feature selection techniques•30 minutes
- Knowledge check: Algorithms and techniques•10 minutes
- Reflection: Setting up your environment in Microsoft Azure•3 minutes
- Reflection: Creating your own code repository•3 minutes
- Knowledge check: Supervised learning•10 minutes
- Reflection: Integrating linear regression•3 minutes
- Reflection: Implementing logistic regression•3 minutes
- Reflection: Implementing decision trees•3 minutes
- Reflection: Implementing and comparing models•3 minutes
- Reflection: Applying metrics and cross-validation•3 minutes
- Knowledge check: Cross-validation and multiple metrics•10 minutes
- Reflection: Implementing backward elimination•3 minutes
- Reflection: Implementing forward selection•3 minutes
- Reflection: Implementing LASSO•3 minutes
- Reflection: Implementing feature selection techniques on a given dataset•3 minutes
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.
What's included
4 videos18 readings9 assignments
4 videos•Total 19 minutes
- Overview of unsupervised learning•3 minutes
- How to implement and visualize clustering•5 minutes
- Use case demonstration of dimensionality reduction•5 minutes
- Walkthrough: Implementing unsupervised learning methods (Optional)•6 minutes
18 readings•Total 355 minutes
- Key principles and approaches to unsupervised learning•10 minutes
- Introduction to clustering techniques•10 minutes
- Practice activity: Implementing k-means clustering•40 minutes
- Walkthrough: Implementing k-means clustering (Optional)•0 minutes
- Practice activity: Implementing DBSCAN clustering•30 minutes
- Walkthrough: Implementing DBSCAN clustering (Optional)•0 minutes
- Practice activity: Implementing clustering and visualization•45 minutes
- Walkthrough: Clustering and visualization (Optional)•0 minutes
- Dimensionality reduction techniques•10 minutes
- Practice activity: Implementing dimensionality reduction techniques•60 minutes
- Walkthrough: Implementing dimensionality reduction techniques (Optional)•0 minutes
- Comparing unsupervised learning approaches for different datasets•10 minutes
- Practice activity: Interpreting clustering and dimensionality reduction outcomes•45 minutes
- Walkthrough: Interpreting clustering and dimensionality reduction outcomes (Optional)•0 minutes
- Discussion: Comparing unsupervised learning approaches for different datasets•30 minutes
- Summary: Unsupervised learning•10 minutes
- Practice activity: Implementing unsupervised learning methods•50 minutes
- Industry exemplar: Application of unsupervised learning techniques•5 minutes
9 assignments•Total 78 minutes
- Graded quiz: Unsupervised learning•30 minutes
- Knowledge check: Unsupervised learning principles•15 minutes
- Reflection: Implementing k-means clustering•3 minutes
- Reflection: Implementing DBSCAN clustering•3 minutes
- Reflection: Implementing clustering and visualization•3 minutes
- Reflection: Implementing dimensionality reduction techniques•3 minutes
- Knowledge check: Dimensionality reduction•15 minutes
- Reflection: Interpreting clustering and dimensionality reduction outcomes•3 minutes
- Reflection: Implementing unsupervised learning methods•3 minutes
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.
What's included
6 videos11 readings6 assignments
6 videos•Total 33 minutes
- Overview of reinforcement learning•5 minutes
- Comparing implementation techniques using Python•6 minutes
- Use case demonstration for applying model evaluation metrics•7 minutes
- Summary of reinforcement learning and other approaches•5 minutes
- Walkthrough: Reinforcement learning and other approaches (Optional)•5 minutes
- Industry exemplar: Reinforcement learning and other approaches•5 minutes
11 readings•Total 375 minutes
- Key principles and approaches of reinforcement learning•20 minutes
- Practice activity: Comparing and reinforcing learning algorithms•90 minutes
- Walkthrough: Comparing Q-learning and policy gradients (Optional)•0 minutes
- Evaluation metrics for reinforcement learning models•15 minutes
- Practice activity: Applying model evaluation metrics in reinforcement learning•90 minutes
- Walkthrough: Applying model evaluation metrics (Optional)•0 minutes
- Comparing reinforcement learning with supervised and unsupervised learning•10 minutes
- Use case demonstration for supervised, unsupervised, and reinforcement learning•20 minutes
- Discussion: Comparative analysis of learning paradigms•30 minutes
- Use case comparison of supervised, unsupervised, and reinforcement learning•10 minutes
- Practice activity: Implementing reinforcement learning and other approaches•90 minutes
6 assignments•Total 69 minutes
- Graded quiz: Reinforcement learning and other approaches•30 minutes
- Knowledge check: Reinforcement learning principles•15 minutes
- Reflection: Q-Learning and Policy Gradients•3 minutes
- Reflection: Model evaluation metrics•3 minutes
- Knowledge check: Evaluation metrics for performance models•15 minutes
- Reflection: Implemented supervised learning, unsupervised learning, and reinforcement learning approaches•3 minutes
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.
What's included
5 videos14 readings8 assignments
5 videos•Total 32 minutes
- Overview of neural networks•5 minutes
- Walkthrough: Implementing and comparing neural network architectures in TensorFlow and PyTorch (Optional)•9 minutes
- Use case demonstration of FNNs, CNNs, and RNNs•5 minutes
- Walkthrough: Analyzing a dataset and implementing a neural network for deep learning analysis (Optional)•6 minutes
- Hear from an expert: Industry exemplar of deep learning and neural networks•7 minutes
14 readings•Total 485 minutes
- Key features and architectures of neural networks•10 minutes
- Implementing neural networks in Azure•15 minutes
- Comparing neural network implementation techniques using Python•15 minutes
- Practice activity: Implementing a neural network with TensorFlow•75 minutes
- Walkthrough: Implementing a neural network with TensorFlow (Optional)•0 minutes
- Practice activity: Implementing and comparing neural network architectures•90 minutes
- Explanation of deep learning techniques•15 minutes
- Practice activity: Implementing deep learning techniques•90 minutes
- Walkthrough: Implementing deep learning techniques (FNN, CNN, RNN) (Optional)•0 minutes
- Implementation of deep learning techniques: GANs and autoencoders•15 minutes
- Practice activity: Evaluating deep learning models in the context of generative AI•105 minutes
- Walkthrough: Evaluating deep learning models in the context of generative AI (Optional)•0 minutes
- Summary: Deep learning and neural networks•10 minutes
- Practice activity: Analyzing a dataset and implementing a neural network for deep learning analysis•45 minutes
8 assignments•Total 75 minutes
- Graded quiz: Deep learning and neural networks•30 minutes
- Knowledge check: Key architectures and features of neural networks•15 minutes
- Reflection: Implementing a neural network with TensorFlow•3 minutes
- Reflection: Implementing and comparing neural network architectures•3 minutes
- Reflection: Implementing deep learning techniques•3 minutes
- Knowledge check: Deep learning techniques•15 minutes
- Reflection: Evaluating deep learning models in the context of generative AI•3 minutes
- Reflection: Analyzing a dataset and implementing a neural network for deep learning analysis•3 minutes
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.
What's included
7 videos16 readings7 assignments1 peer review
7 videos•Total 33 minutes
- Overview of AI/ML engineering approaches•6 minutes
- Hear from an expert: Aligning AI with organizational goals•4 minutes
- The Importance of collaboration in AI/ML professions•4 minutes
- Hear from an expert: Balancing business and technical priorities•7 minutes
- Summary: AI/ML engineering and working with models•6 minutes
- Summary, thank you, and good luck•3 minutes
- Thank you, and congratulations!•2 minutes
16 readings•Total 355 minutes
- Real-world case studies of corporate AI/ML implementations•10 minutes
- Practice activity: Implementing a corporate approach in context•30 minutes
- Practice activity: Deploying and repairing AI/ML systems•30 minutes
- Walkthrough: Deploying and repairing AI/ML systems (Optional)•0 minutes
- The roles of AI/ML engineers•10 minutes
- Detailed role descriptions of AI/ML engineers in industry•10 minutes
- Discussion: Comparing AI/ML engineer roles•30 minutes
- Considering your career in AI/ML engineering•10 minutes
- Practice activity: Identifying your strengths, weaknesses, and interests in AI/ML engineering•30 minutes
- Understanding team dynamics in AI/ML development teams•10 minutes
- Comprehensive guide•5 minutes
- Tools and platforms for further learning•5 minutes
- Industry exemplar: Discussing roles in AI/ML•10 minutes
- Practice activity: Creating an AI/ML development plan for a fictitious project•45 minutes
- Walkthrough: Creating an AI/ML development plan for customer churn prediction (Optional)•0 minutes
- Practice activity: Designing and developing an AI/ML solution•120 minutes
7 assignments•Total 72 minutes
- Graded quiz: AI/ML engineering and working with models•30 minutes
- Reflection: Deploying and repairing AI/ML systems•3 minutes
- Knowledge check: AI/ML engineering approaches•15 minutes
- Knowledge check: Matching AI/ML engineering roles to responsibilities•15 minutes
- Reflection: Identifying your strengths, weaknesses, and interests in AI/ML engineering•3 minutes
- Reflection: Creating an AI/ML development plan for a fictitious project•3 minutes
- Knowledge check: Designing and developing an AI/ML solution•3 minutes
1 peer review•Total 45 minutes
- Course assignment: Producing a comprehensive AI/ML project technical report•45 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
Explore more from Software Development
- Status: Free Trial
Course
- Status: Free TrialF
Fractal Analytics
Course
- Status: Free Trial
- Status: Free TrialJ
Johns Hopkins University
Course
Why people choose Coursera for their career
Learner reviews
- 5 stars
80.30%
- 4 stars
13.63%
- 3 stars
0%
- 2 stars
3.03%
- 1 star
3.03%
Showing 3 of 66
Reviewed on Mar 31, 2025
It was very well tailored for all types of learners.
Frequently asked questions
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.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.