What Is Programming? And How To Get Started
January 28, 2025
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This course is part of Microsoft AI & ML Engineering Professional Certificate
Instructor: Microsoft
Included with
Recommended experience
Intermediate level
You should have completed the first course in the program, or have equivalent experience with the concepts taught in those courses.
Recommended experience
Intermediate level
You should have completed the first course in the program, or have equivalent experience with the concepts taught in those courses.
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November 2024
45 assignments
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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.
9 videos30 readings15 assignments
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.
4 videos18 readings9 assignments
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.
6 videos11 readings6 assignments
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.
5 videos14 readings8 assignments
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.
7 videos16 readings7 assignments1 peer review
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.
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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.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
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