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The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. It provides a broad introduction to modern machine learning. Topics include supervised learning, deep learning, decision trees, and more.  

Organization: Stanford School of Engineering

Instructor: Andrew Ng et al.

Platform: Coursera

Schedule: On demand

Time commitment: 2 months at 10 hours a week 

Courses/modules: 3

Topic: AI/ML

Healthcare focus: No

Prerequisite: No prior knowledge required

Language: English

Fee to register

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Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals. You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets. In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations. 

Organization: IBM

Instructor: Romeo Kienzler et al.

Platform: Coursera

Schedule: On demand

Time commitment: 1 month at 10 hours a week

Courses/modules: 4

Topic: Data science

Healthcare focus: No

Prerequisite: No prior knowledge required

Language: English

Fee to register

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Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 4 months. No prior knowledge of computer science or programming languages is required. Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater. You’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools, and libraries including Python, SQL, Jupyter notebooks, GitHub, RStudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more. Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd. This program is ACE® and FIBAA recommended — when you complete, you can earn up to 12 college credits and 6 ECTS credits.

Organization: IBM

Instructor: IBM Skills Network Team

Platform: Coursera

Schedule: On demand

Time commitment: 4 months at 10 hours a week

Courses/modules: 12

Topic: Data science

Healthcare focus: No

Prerequisite: No prior knowledge required

Language: English

Fee to register

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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering ng data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Organization: University of Michigan

Instructor: Christopher Brooks et al.

Platform: Coursera

Schedule: On demand

Time commitment: 4 months at 10 hours a week

Courses/modules: 5

Topic: Data science

Healthcare focus: No

Prerequisite: Some related experience required

Language: English

Fee to register

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In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

By taking this course, you'll learn to:

• Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
• Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
• Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements
• Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
• Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.

This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.

Organization: DeepLearning.AI & Amazon Web Services

Instructor: Chris Fregly et al.

Platform: Coursera

Schedule: On demand

Time commitment: 3 weeks at 4 hours a week

Courses/modules: N/A

Topic: LLM

Healthcare focus: No

Prerequisite: Recommend experience

Language: English

Fee to register

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ChatGPT and other large language models are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it. This course will teach you how to be an  expert user of these generative AI tools. The course will show amazing examples of how you can tap into these generative AI tools' emergent intelligence and reasoning, how you can use them to be more productive day to day, and give you insight into how they work.

Large language models respond to instructions and questions posed by users in natural language statements, known as “prompts”. Although large language models will disrupt many fields, most users lack the skills to write effective prompts. Expert users, who understand how to write good prompts, are orders of magnitude more productive and can unlock significantly more creative uses for these tools.

This course introduces students to the patterns and approaches for writing effective prompts for large language models.  Anyone can take the course and the only required knowledge is basic computer usage skills, such as using a browser and accessing ChatGPT. Students will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. By the end of the course, students will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming.

Organization: Vanderbilt University

Instructor: Dr. Jules White et al.

Platform: Coursera

Schedule: On demand

Time commitment: 18 hours

Courses/modules: 6

Topic: LLM

Healthcare focus: No

Prerequisite: Recommend experience

Language: English

Fee to register

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Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.

In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.

This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.

CME Accreditation

The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. View the full CME accreditation information on the individual course FAQ page.

Applied Learning Project

The final course will consist of a capstone project that will take you on a guided tour exploring all the concepts we have covered in the different classes. This will be a hands-on experience following a patient's journey from the lens of the data, using a unique dataset created for this specialization.We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model.

Organization: Stanford

Instructor: Matthew Lungren et al.

Platform: Coursera

Schedule: On demand

Time commitment: 1 month at 10 hours a week

Courses/modules: 5

Topic: AI/ML

Healthcare focus: Yes

Prerequisite: No prior knowledge required

Language: English

Fee to register

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This course explains what is meant by bias in the context of machine learning algorithms, how bias can be present in the training data, and how it can be unintentionally introduced in the learning phase. Learners will gain an appreciation of why this is a concern and why it needs to be addressed.

Organization: The Alan Turing Institute

Instructor: Adriano Koshiyama et al.

Platform: The Alan Turing Institute

Schedule: On demand

Time commitment: 20 hours

Courses/modules: 5

Topic: AI/ML

Healthcare focus: No

Prerequisite: No prior knowledge required for Milestones 1 and 2

Language: English

Free

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This course is alternatively titled "How learning to lie with data is essential to prevent AI from being sexist and racist." This is intended to catch attention but also highlights the content of this course which intends to support data scientists looking to do responsible AI. The first part of the title comes from a book from 1954 titled "How to lie with statistics" which has been brought back into consciousness through another book "Rebooting AI".

The first part of this course presents elements of how data can be misleading, while providing concrete tips to identify and address these data issues. The second part of the title refers to a series of recent scandals where it is argued that AI has not been used responsibly. These scandals, some of which are used as case studies in this course, are leading to the legislation coming in to ensure ethical uses of AI.

The second half of this course is focused on these ethical considerations needed for using AI responsibly. The course aims to support Data Scientists and their managers to increase their understanding of potential ethical challenges in the application of AI and provide concrete tips to support them to be responsible.

This course has been commissioned as part of our open funding call for Responsible AI courses, with funding from Accenture and the Alan Turing Institute.

Organization: The Alan Turing Institute

Instructor: Dr Lily Clements et al.

Platform: The Alan Turing Institute

Schedule: On demand

Time commitment: 40 hours

Courses/modules: 4

Topic: AI/ML

Healthcare focus: No

Prerequisite: Not specified

Language: English

Free

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This course is for you if:

• You are a complete/near complete beginner to programming

• Work in health-related sector as either clinical, administrative or academic staff - for example as a nurse, health administrator, consultant, biomedical researcher, etc.

• You require flexible learning format

• You need to start using more data for your current or future role

• You want to be able to perform basic tasks in Python, such as making plots and sorting through tables, based on health-data without getting too deep into the theory of coding

Learning Aims:

By the end of this module, you will be able to use Python to import and manipulate datasets, and be confident in performing programming operations such as using conditionals, loops, and functions, as well as conducting basic statistical analyses

Organization: King’s Health Partners, King’s College London

Instructor: Not specified

Platform: Futures HDRUK

Schedule: On demand

Time commitment: 20 hours

Courses/modules: 4

Topic: Data Science

Healthcare focus: Yes

Prerequisite: No prior knowledge required

Language: English

Free

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Part 1 of 3: "AI 101 for Nonprofits" – This is the first video in a three-part virtual bootcamp series created by the Fund for the City of New York for OpenAI Academy. Designed for nonprofit and community-based organizations, this session introduces the basics of artificial intelligence—what it is, how it works, and why it matters. No technical background required. Learn how AI can expand the capacity of the nonprofit sector and what it could mean for your organization.

Organization: OpenAI

Instructor: Rich Leimsider

Platform: OpenAI Academy

Schedule: On demand

Time commitment: 3 hours

Courses/modules: 3

Topic: AI/ML

Healthcare focus: No

Prerequisite: No prior knowledge required

Language: English

Free

In this five-week online, instructor-led course, participants will learn about the different AI tools available for medical applications, including Large Language Models. They will also delve into different aspects of using AI tools in clinical practice, such as medicolegal and data privacy risks and ethical considerations. This course is intended for health professionals such as physicians and clinicians and assumes no prior knowledge of AI or coding. It is not meant to be a technical course dealing with coding. Rather, it is a practical course on how to understand and apply readily available AI technologies to day-to-day practice.

Organization: University of Toronto

Instructor: Dr. Nihal Haque

Platform: Temerty Centre for Artificial Intelligence Research and Education in Medicine

Schedule: Online

Time commitment: 10 hours

Courses/modules: 5

Topic: AI/ML

Healthcare focus: Yes

Prerequisite: No prior knowledge required

Language: English

Fee to register