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Machine Learning is a subfield of Artificial intelligence
"Learning machines to imitate human intelligence"
Machine Learning (ML)

Traditional programming uses known algorithms to produce results from data:

Data + Algorithms = Results

Machine learning creates new algorithms from data and results:

Data + Results = Algorithms

Neural Networks (NN)

Neural Networks is:

A programming technique

A method used in machine learning

A software that learns from mistakes

Neural Networks are based on how the human brain works:
Neurons are sending messages to each other. While the neurons are trying to solve a problem (over and over again), it is strengthening the connections that lead to success and diminishing the connections that lead to failure.

Perceptrons

The Perceptron defines the first step into Neural Networks.

It represents a single neuron with only one input layer, and no hidden layers.

Learn how to program a perceptron.

Neural Networks

Neural Networks are Multi-Layer Perceptrons.

In its simplest form, a neural network is made up from:

An input layer (yellow)

A hidden layer (blue)

An output layer (red)

In the Neural Network Model, input data (yellow) are processed against a hidden layer (blue) before producing the final output (red).

The First Layer:
The yellow perceptrons are making simple decisions based on the input. Each single decision is sent to the perceptrons in the next layer.

The Second Layer:
The blue perceptrons are making decisions by weighing the results from the first layer. This layer make more complex decisions at a more abstract level than the first layer.

Deep Neural Networks

Deep Neural Networks is:

A programming technique

A method used in machine learning

A software that learns from mistakes

Deep Neural Networks are made up of several hidden layers of neural networks that perform complex operations on massive amounts of data.

Each successive layer uses the preceding layer as input.

For instance, optical reading uses low layers to identify edges, and higher layers to identify letters.

In the Deep Neural Network Model, input data (yellow) are processed against a hidden layer (blue) and modified against more hidden layers (green) to produce the final output (red).

The First Layer:
The yellow perceptrons are making simple decisions based on the input. Each single decision is sent to the perceptrons in the next layer.

The Second Layer:
The blue perceptrons are making decisions by weighing the results from the first layer. This layer make more complex decisions at a more abstract level than the first layer.

The Third Layer:
Even more complex decisions are made by the green perceptrons.

Deep Learning (DL)

Deep Learning is a subset of Machine Learning.

Deep Learning is responsible for the AI boom of the last years.

Deep learning is an advanced type of ML that handles complex tasks like image recognition.

Machine Learning

Deep Learning

A subset of AI

A subset of Machine Learning

Uses smaller data sets

Uses larger datasets

Trained by humans

Learns on its own

Creates simple algorithms

Creates complex algorithms

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W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.

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Artificial Intelligence: Distinguishing Between Fact and Fiction

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Artificial Intelligence: Distinguishing Between Fact and Fiction

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Is artificial intelligence a reality or just marketing hype?

On this two-week course, you will have the opportunity to explore artificial intelligence, how it's perceived today and the differences between the hype, marketing and reality of AI.

You will have the chance to examine Alan Turing's scholarship following his codebreaking successes in World War II at Bletchley Park. You will also explore how John McCarthy coined the term 'artificial intelligence (AI)' and the current state of the art, including manufacturing and social robotics.

What topics will you cover?

Roots of artificial intelligence (AI)

Different features of human intelligence

Asimov's laws

The reality of AI

Learning on this course

You can take this self-guided course and learn at your own pace. On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

Explain the different features of human intelligence

Distinguish between the claims and current reality of artificial intelligence (AI)

By the end of the course, you'll be able to...

Who is the course for?

This course is for anyone interested in artificial intelligence in psychology, philosophy and child development.
Please note that the individuals detailed in the 'Who will you learn with?' section below, are current staff members and may be subject to change.

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Experience the power of social learning, and get inspired by an international network of learners

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If you enjoy this course you can apply to join the program it's part of to start working towards a degree on FutureLearn.

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Join and complete the program

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By the end of the course, you'll be able to...

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funded by

Learning on FutureLearn

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Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

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Introduction to Microsoft Azure AI Fundamentals

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AI for Healthcare: Equipping the Workforce for Digital Transformation

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What do we mean by artificial intelligence? How will artificial intelligence transform healthcare? Can artificial intelligence give the gift of time back to health care professionals to focus on caring for patients and less time on mundane and repetitive administrative tasks? Is artificial intelligence a replacement or a colleague? These are just some of the questions you'll be encouraged to explore in this course. The University of Manchester and Health Education England have come together to deliver a MOOC on artificial intelligence. The Topol Review published in February 2019, outlined that within 20 years, 90% of all jobs in the National Health Service will require some elements of digital skills. But where are we now?

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Using AI to Gain a Competitive Edge for your Customer Experience (CX)

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Learning on this course

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What will you achieve?

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What topics will you cover?

Histories of racism within technology development

How technologies can discriminate on the grounds of race, and the impacts this can have on Black people and people of colour

The need for anti-racism in the technology sector

Anti-racist design, marketing and communication

Anti-racist approaches to the product development lifecycle, from recruitment and user research through to implementation, testing and launch

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you'll be able to...

Who is the course for?

This course has been designed for anyone interested in creating or advocating for anti-racism in technology development. It will be of particular interest if you're considering a career or already work in the technology sector.

You may also find this course appealing if you're a student in a technology-based degree such as Computer Science or Design, and you're looking to complement your technical and subject knowledge.

Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning

Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities

Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners

Share ideas with your peers and course educators on every step of the course

Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning

Whenever you're ready, mark each step as complete, you're in control

Complete 90% of course steps and all of the assessments to earn your certificate

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How to Create Anti-Racist Technologies - Online Diversity Course - FutureLearn

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Technologies have become inseparable from our daily lives and we are more reliant on them than ever before. But technologies have been designed within a predominantly white technology sector, and many of them reinforce racism. On this course, you'll delve into the history of racism within technology development and the different ways you can combat it in the future and bring about more inclusive technology.

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Explore the present and future legal challenges posed by data and AI

In 2020, digital data drives our economy and artificial intelligence (AI) is becoming an increasing part of our everyday lives.

On this three-week course, you'll be introduced to the legal concepts that underpin the internet economy and the potential liability of various online platforms.

The course will teach you how to structure your legal analysis of issues relating to personal data and digital intellectual property as a legal professional.

You'll also discover the challenges that new technologies present to the legal profession and what future laws we may need.

What topics will you cover?

Personal data laws

Digital intellectual property

Liability of online platforms

Laws applicable to AI

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

Evaluate the General Data Protection Regulations

Identify the data protection laws

Describe the liability of online platforms

Evaluate the new copyright directive

Investigate the law associated with Artificial Intelligence

By the end of the course, you'll be able to...

Who is the course for?

This course is designed for postgraduate students with an interest in both law and technology.

It will be of interest to both qualified lawyers and those recently completing their qualifications who want an understanding of the key legal concepts underpinning the technologies of the fourth industrial revolution.

It will also be useful for developers, technologists, engineers and other professionals with digital expertise who want to understand the laws relevant to their areas.

Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning

Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities

Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners

Share ideas with your peers and course educators on every step of the course

Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning

Whenever you're ready, mark each step as complete, you're in control

Complete 90% of course steps and all of the assessments to earn your certificate

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The Laws of Data and AI - Online Course - FutureLearn

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In 2020, digital data drives our economy and artificial intelligence (AI) is becoming an increasing part of our everyday lives. On this three-week course, you'll be introduced to the legal concepts that underpin the internet economy and the potential liability of various online platforms. The course will teach you how to structure your legal analysis of issues relating to personal data and digital intellectual property as a legal professional.

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Introduction to Creative AI - Online Course - FutureLearn

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Discover how AI is transforming the creative industries with this online creative computing course from UAL offering practical examples and skills guides.

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Intelligent Systems: An Introduction to Deep Learning and Autonomous Systems

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Intelligent Systems: An Introduction to Deep Learning and Autonomous Systems

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Intro to Deep Learning and Autonomous Systems - Machine Learning Course

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Discover the benefits and risks of deep learning and its uses in systems such as assistive technology and facial recognition with this online course from the University of York.

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Mathematics for Computer Science: Essential Skills

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Mathematics for Computer Science: Essential Skills

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Gain confidence in mathematics to kickstart your career in computer science

If you are approaching or considering degree-level study in computer science, you may wish to strengthen your understanding of key mathematical techniques.

This course from the University of Hull is designed to refresh or equip you with knowledge of the foundational concepts of mathematics for computer science

It assumes no prior mathematical knowledge, so provides a gentle introduction to potentially daunting mathematical skills. Irrespective of academic background or prior attainment, this mathematics bridging course can help you realise your ambition of studying computer science.

Gain confidence in foundational mathematics for computer science

This course will cover some of the key mathematical concepts needed for computer science.

Topics covered include drawing and interpreting Venn diagrams of set relations and operations, algebra techniques, using set theory notation to find subsets and complements, and more.

Equipped with these skills, you'll be capable of approaching your degree or equivalent study in computer science with assurance.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you'll be able to...

Produce and interpret Venn diagrams of set relations and operations, using them to solve problems. Also use set theory notation and be able to find subsets and complements, define elements and find results of intersections or unions.

Solve linear, quadratic and simultaneous equations and inequalities using a variety of algebra techniques.

Explore addition and subtraction of vectors, multiplication by a scalar and find the magnitude and direction of a vector. Perform the matrix operations of addition, multiplication and transposition and find the inverse of a given matrix if it exists.

Who is the course for?

This course is aimed at learners starting or considering studying computer science at degree level, who want to build confidence in key mathematical concepts.

It assumes no prior mathematical knowledge, so is suitable for learners of all backgrounds and previous educational attainment.

Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning

Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities

Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners

Share ideas with your peers and course educators on every step of the course

Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning

Whenever you're ready, mark each step as complete, you're in control

Complete 90% of course steps and all of the assessments to earn your certificate

# data[].attributes.attachment_link.title
Mathematics for Computer Science: Essential Skills - FutureLearn

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If you are approaching or considering degree-level study in computer science, you may wish to strengthen your understanding of key mathematical techniques. This course from the University of Hull is designed to refresh or equip you with knowledge of the foundational concepts of mathematics for computer science It assumes no prior mathematical knowledge, so provides a gentle introduction to potentially daunting mathematical skills.

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How to Create Gender-Inclusive Technology

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How to Create Gender-Inclusive Technology

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How to Create Gender-Inclusive Technology - Online Course - FutureLearn

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Explore how to create and advocate for gender-inclusive technologies to promote equality in society with this course from The Creative Computing Institute.

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Discover the fundamentals of software development

Software development is a continuously growing industry with many opportunities for employment.

On this four-week course, you'll master the basics of Python, a relatively simple programming language that is widely used in multiple industries.

Lay the foundations of programming

You'll start this course by examining Python and how it compares to other programming languages. Then, you'll start your journey to becoming a Python programmer by exploring the standards and conventions of the language and running a simple Python function test.

Learn the basic functions of Python

Through this course, you'll gain immediate practical benefits for your career growth and professional development by building your programming skills in Python.

The second week of this course will guide you through the basics of the programming language, looking at variables, logic, functions, and loops. You'll also be given practical tasks to complete at the end of each section to truly cement your new skills.

Explore how to improve your programs

Programming, at its core, is about problem-solving. It requires a lot of concentration, effort, and energy, so programmers are always looking for ways to be more efficient. That's where debugging and version control come in.

You'll look at the different types of errors that you can encounter while programming and the ways that you can approach debugging. Then, you'll explore version control, specifically using Git and GitHub, which is a crucial tool in programming, especially when working as part of a development team.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you'll be able to...

Engage with Python tools and resources to aid effective programming.

Create simple programs with basic Python elements.

Improve Python programs with debugging, exception handling, and version control.

Apply object-oriented programming principles to structure Python programs.

Who is the course for?

This course is designed for anyone interested in starting a career in programming, specifically using the Python programming language.

The course will build your foundational knowledge to pursue a number of roles such as web back-end development, application development, machine learning, or data analytics.

In collaboration with

Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning

Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities

Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners

Share ideas with your peers and course educators on every step of the course

Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning

Whenever you're ready, mark each step as complete, you're in control

Complete 90% of course steps and all of the assessments to earn your certificate

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Introduction to Programming with Python - Online Software Development Course

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Software development is a continuously growing industry with many opportunities for employment. On this four-week course, you'll master the basics of Python, a relatively simple programming language that is widely used in multiple industries. You'll start this course by examining Python and how it compares to other programming languages.

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Explore computer programming and complete block-language programming exercises

What are computers good at and how can you get the best out of them?

On this course, you'll find out how and why people program computers.

Using an introductory block-language for coding, you'll discover the kinds of problems that computers can help you solve.

You'll also explore different types of coding languages and discover a variety of useful applications of code.

At the end of the course, you'll implement key principles you've learned by writing your first computer programs.

Featured in the UK government's The Skills Toolkit collection.

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Rachel Skelton: Have you thought about a career in tech, but weren't sure where to begin? This course will help you find out. Computer Programming For Everyone is one of three courses which introduces you to coding and design, from understanding some of the key developments in the advent of computer technology to describing the features of text-based languages. You'll explore some of the basics behind how programming works. You'll also see how people use the skills in the digital workplace. This course group is part of our portfolio of digital and professional skills courses, which covers a diverse range of subjects, from coding to remote working, all available for free to help you begin building your career today.

Whether you're new to the world of work or forging a new career for yourself, we look forward to sharing your journey into the fast moving digital space. Join us online now.

What topics will you cover?

Key developments in computing

Explore what code can do

How programming works in real life

Writing basic code

Solving a more complex problem with code

Finding out what a development team does

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you'll be able to...

Who is the course for?

This course is designed for people who have digital skills but would like to build confidence in using digital technology in the workplace.

This may include those studying for A-levels, those who have a degree, or those who want to improve their career prospects.

The course will also be useful for anyone in employment who wants to boost their digital skills or anyone returning to the workplace who'd like to gain confidence in how working practices have evolved.

You might also be interested in the other courses in the Digital Skills for the Workplace collection from the University of Leeds and the Institute of Coding.

Endorsers and supporters

supported by

supported by

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Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning

Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities

Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners

Share ideas with your peers and course educators on every step of the course

Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning

Whenever you're ready, mark each step as complete, you're in control

Complete 90% of course steps and all of the assessments to earn your certificate

# data[].attributes.attachment_link.title
Computer Programming for Everyone - Online Course - FutureLearn

# data[].attributes.attachment_link.description
What are computers good at and how can you get the best out of them? On this course, you'll find out how and why people program computers. Using an introductory block-language for coding, you'll discover the kinds of problems that computers can help you solve.

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Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course,part ofour Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

The basics of machine learning

How to perform cross-validation to avoid overtraining

Several popular machine learning algorithms

How to build a recommendation system

What is regularization and why it is useful?

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By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX's instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University's stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.

Please read the edX Privacy Policy for more information regarding the processing, transmission, and use of data collected through the edX platform.

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Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

# data[].attributes.attachment_link.description
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

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Machine Learning with Python: A Practical Introduction

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Machine Learning with Python: A Practical Introduction

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There is one session available:

137,952 already enrolled!

Progress at your own speed

There is one session available:

Please Note: Learners who successfully complete this IBM course can earn a skill badge - a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this course. Enroll to learn more, complete the course and claim your badge!
This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you'll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

Show more

Awards

At a glance

Language: English

Video Transcript: English

Associated programs:

The difference between the two main types of machine learning methods: supervised and unsupervised

Supervised learning algorithms, including classification and regression

Unsupervised learning algorithms, including Clustering and Dimensionality Reduction

How statistical modeling relates to machine learning and how to compare them

Real-life examples of the different ways machine learning affects society

Show more

Module 1 - Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning

Module 2 - Regression
Linear Regression
Non-linear Regression
Model evaluation methods

Module 3 - Classification
K-Nearest Neighbour
Decision Trees
Logistic Regression
Support Vector Machines
Model Evaluation

Module 4 - Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering

Module 5 - Recommender Systems
Content-based recommender systems
Collaborative Filtering

Show more

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

# data[].attributes.attachment_link.title
Machine Learning with Python: A Practical Introduction

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137,952 already enrolled! Progress at your own speed There is one session available: Please Note: Learners who successfully complete this IBM course can earn a skill badge - a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this course.

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What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.

TinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise.

The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the "language" behind TinyML and be ready to dive into the application of TinyML in future courses.

Following Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used Tiny ML applications-such as tiny devices and smartphones-and deploy code to your own physical TinyML device. Fundamentals of TinyML provides an introduction to TinyML and is not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.

Fundamentals of Machine Learning (ML)

Fundamentals of Deep Learning

How to gather data for ML

How to train and deploy ML models

Understanding embedded ML

Responsible AI Design

Chapter 1: Welcome to TinyML

Chapter 1.1: Course Overview

Chapter 1.2: The Future of ML is Tiny and Bright

Chapter 1.3: TinyML Challenges

Chapter 1.4: Getting Started

Chapter 2: Introduction to (Tiny) ML

Chapter 2.1: The Machine Learning Paradigm

Chapter 2.2: The Building Blocks of Deep Learning

Chapter 2.3: Exploring Machine Learning Scenarios

Chapter 2.4: Building a Computer Vision Model

Chapter 2.5: Responsible AI Design

Chapter 2.6: Summary

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

# data[].attributes.attachment_link.description
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field. TinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software.

# data[].attributes.attachment_link.readable_text
There is one session available:

104,181 already enrolled!

Progress at your own speed

Optional upgrade available

There is one session available:

Please Note: Learners who successfully complete this IBM course can earn a skill badge - a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this course. Enroll to learn more, complete the course and claim your badge!
In this course, you will learn what AI is and understand its applications and use cases and how it is transforming our lives. You will explore basic AI concepts including machine learning, deep learning, and neural networks as well as use cases and applications of AI. You will be exposed to concerns surrounding AI, including ethics, bias, jobs and the impacts on society.

You will take a glimpse of the future with AI, get advice for starting an AI related career, and wrap up the course by demonstrating AI in action with a mini project.

This AI for Everyone course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Show more

Awards

At a glance

Video Transcript: English

Associated programs:

Understand what is AI, its applications and use cases and how it is transforming our lives

Explain terms like Machine Learning, Deep Learning, and Neural Networks

Describe several issues and ethical concerns surrounding AI

Articulate advice from experts about learning and starting a career in AI

Show more

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX for Business.

# data[].attributes.attachment_link.description
104,181 already enrolled! Progress at your own speed Optional upgrade available There is one session available: Please Note: Learners who successfully complete this IBM course can earn a skill badge - a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this course.

# data[].attributes.attachment_link.readable_text
There is one session available:

Google AI for Anyone teaches you about what Artificial Intelligence is. You'll cut through the hype and learn about AI and Machine Learning.

As its name suggests, this course is for anybody -- you don't need a computer science, mathematics or AI background to understand it. No programming skills or prior knowledge are needed.

We'll take you through, from first principles what the fuss is all about, and you'll get hands-on in playing with data to teach a computer how to recognize images, sounds and more.

As you explore how AI is used in the real world (recommender systems, computer vision, self-driving etc.) you will also begin to build an understanding of Neural networks and the types of machine learning including supervised, unsupervised, reinforcement etc. You will also see (and experience) what programming AI looks like and how it is applied.

From here you will be able to continue your journey through the emerging fields of AI and ML and related technologies. In so doing, you will formulate a basis to understand and discuss AI and ML related matters in your personal and professional life.

What AI is and isn't

How AI, ML, Deep Learning all fit together

Why Data is important

Applications of AI

What programming AI looks like - predicting numbers with regression, computer-assisted decisions with classification, gaming etc can make mistakes because of poor data

Neural Networks -- what they are and what they aren't. Basics. Forward and Backward propagation

Understand how Fairness and Ethics work in AI

The process of teaching a computer how to learn

How AI applications can make mistakes because of poor data

Chapter 1.1 - What is AI?

Chapter 1.2 - Let's talk terminology and understand what AI, ML, DL are all about

Chapter 1-3 Ethics and Fairness

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

# data[].attributes.attachment_link.description
There is one session available: Google AI for Anyone teaches you about what Artificial Intelligence is. You'll cut through the hype and learn about AI and Machine Learning. As its name suggests, this course is for anybody -- you don't need a computer science, mathematics or AI background to understand it.

# data[].attributes.attachment_link.readable_text
There is one session available:

Welcome to the Intro to ChatGPT course, where we will explore the capabilities of OpenAI's advanced language model and learn how to utilize it for a wide range of applications. Throughout this course, you will gain a comprehensive understanding of ChatGPT, its features, and limitations.

In Module 1, we will begin by getting started with ChatGPT. You will be introduced to what ChatGPT is, how it works, and its capabilities. We will cover the basics of creating an account in OpenAI and provide an overview of what ChatGPT can be used for. Additionally, we will discuss the limitations of ChatGPT, including its only being trained on data up to 2021 and how it handles conversation history. We will then delve into the art of asking questions to ChatGPT and learning from its responses, including how to summarize and expand on a topic, as well as handling abusive queries.

In Module 2, we will enhance your ChatGPT experience by customizing it to your liking. You will learn how to personalize your conversations with ChatGPT by customizing its responses and adjusting its tone. We will also explore ChatGPT's conversation history and how you can use it to keep track of past conversations. In addition, we will cover advanced techniques such as using ChatGPT for productivity, building chatbots with ChatGPT, and integrating ChatGPT with other tools.

In Module 3, we will master ChatGPT by exploring advanced applications and best practices. You will learn how to use ChatGPT for language translation, generating creative content, and discovering new knowledge. We will also discuss ChatGPT's limitations and how to maintain ethical standards when using it.

Additionally, we will cover best practices for maximizing the potential of ChatGPT, including understanding its limitations and effectively contextualizing queries.

Throughout this course, you will have the opportunity to practice what you learn by interacting with ChatGPT and exploring its various features. We will provide examples and exercises to help you master the techniques and applications covered in each section. By the end of this course, you will have a thorough understanding of ChatGPT and how to use it to your advantage.

Enroll now and begin your journey towards mastering ChatGPT.

Understand what ChatGPT is and its capabilities

Learn to create an account and get started with ChatGPT

Explore the art of asking questions to ChatGPT and handling abusive queries

Personalize your ChatGPT experience by customizing its responses and adjusting its tone

Discover ChatGPT's conversation history and its usage

Build chatbots with ChatGPT and integrate it with other tools

Use ChatGPT for language translation, generating creative content, and discovering new knowledge

Understand ChatGPT's limitations and ethical standards when using it

Effectively contextualize queries and maximize ChatGPT's potential

Practice interacting with ChatGPT through examples and exercises.

Course Description:

In this course, you will explore the capabilities of OpenAI's advanced language model, ChatGPT, and learn how to utilize it for a wide range of applications. You will gain a comprehensive understanding of ChatGPT, its features, and limitations. By the end of the course, you will be able to use ChatGPT for various tasks, such as summarizing and expanding on a topic, building chatbots, and generating creative content.

Course Outline:

Module 1: Getting started with ChatGPT

● Introduction to ChatGPT and its capabilities

● Creating an account in OpenAI

● Overview of what ChatGPT can be used for

● Limitations of ChatGPT, including its only being trained on data up to 2021 and how it handles conversation history

● The art of asking questions to ChatGPT and learning from its responses

● How to summarize and expand on a topic with ChatGPT

● Handling abusive queries

Module 2: Customizing ChatGPT

● Personalizing conversations with ChatGPT by customizing its responses and adjusting its tone

● Exploring ChatGPT's conversation history and using it to keep track of past conversations

● Using ChatGPT for productivity, such as scheduling and task management

● Building chatbots with ChatGPT

● Integrating ChatGPT with other tools, such as Slack and Discord

Module 3: Advanced Applications and Best Practices

● Using ChatGPT for language translation

● Generating creative content with ChatGPT

● Discovering new knowledge with ChatGPT

● ChatGPT's limitations and how to maintain ethical standards when using it

● Best practices for maximizing the potential of ChatGPT, including understanding its limitations and effectively contextualizing queries

Assessment:

● Quizzes and exercises at the end of each section to test your understanding of the concepts covered in the course

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This course provides a practical introduction to ChatGPT, from signing up to mastering its advanced features. Topics covered include conversing with ChatGPT, customizing it, using it for productivity and building chatbots, as well as advanced applications like language translation and generating creative content. Best practices and tips for using ChatGPT are also included.

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Learn the foundations of how computers understand processing and produce human languages – Natural Language Processing (NLP), a science at the intersection of computer science, artificial intelligence and linguistics.

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There is one session available:

Artificial Intelligence is everywhere. Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies.

However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives.

This course is designed to give you an introduction to the amazing world of Artificial Intelligence. It offers a very pragmatic overview of AI fundamentals, accessible to both technical and non-technical audiences. This course provides an entrance to the amazing Linux Foundation AI & Data ecosystem, which will be very useful for people looking for relevant open source tools or areas to get involved to continue developing new data and AI skills.

Data and AI Fundamentals is geared towards professionals and students looking for new AI skills, including company executives, hiring managers, product managers, and developers. This course would also be beneficial for industry professionals coming from diverse industries such as finance, supply chain, manufacturing, and other verticals.

It examines the different kinds of AI technologies (e.g., machine learning, NLP). It discusses how to enumerate typical AI use cases for a variety of industries and identifies potential AI career opportunities. Learn to navigate the rich set of Linux Foundation AI & Data open source projects and tools throughout the course.

This course prepares students with the ability to identify the different options available from the family of AI technologies. Upon completing this course, you will be able to choose suitable AI techniques depending on the business needs and leverage existing AI projects and tools from the LF AI & Data ecosystem.

Differentiate various kinds of AI technologies (e.g., machine learning, NLP)

Enumerate typical AI use cases for a variety of industries

Identify potential AI career opportunities

Navigate the rich set of Linux Foundation AI & Data open source projects and tools

Welcome!

Chapter 1. Introduction to Artificial Intelligence

Chapter 2. Related AI Topics

Chapter 3. LF AI & Data Foundation

Final Exam (Verified Certificate track only)

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

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Learn the fundamentals of Artificial Intelligence with a combination of AI fundamentals and an overview of the rich Linux Foundation AI & Data projects ecosystem. It is well-suited for any kind of AI adopter, regardless of the professional background and level of technical knowledge.

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CS50's Introduction to Artificial Intelligence with Python

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CS50's Introduction to Artificial Intelligence with Python

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There is one session available:

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50's Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You'll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

graph search algorithms

adversarial search

knowledge representation

logical inference

probability theory

Bayesian networks

Markov models

constraint satisfaction

machine learning

reinforcement learning

neural networks

natural language processing

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.

# data[].attributes.attachment_link.title
CS50's Introduction to Artificial Intelligence with Python

# data[].attributes.attachment_link.description
There is one session available: AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly.

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This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you'll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You'll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Apply feature engineering techniques using Python -Construct a Naive Bayes model -Describe how unsupervised learning differs from supervised learning -Code a K-means algorithm in Python -Evaluate and optimize the results of K-means model -Explore decision tree models, how they work, and their advantages over other types of supervised machine learning -Characterize bagging in machine learning, specifically for random forest models -Distinguish boosting in machine learning, specifically for XGBoost models -Explain tuning model parameters and how they affect performance and evaluation metrics

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This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you'll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions.

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Offered by University of London. Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of ... Enroll for free.

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Data Literacy Courses - The Data Literacy Project

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Access a range of instructor-led and self-paced courses to develop your fundamental understanding of data, both in everyday life and within your organization. Take things further with advanced learning opportunities and assessments to receive our industry recognized, Certified Data Literacy certificate.

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Computers are becoming smarter and may soon become intelligent. This free course, Machines, minds and computers, looks at what intelligence is, how computers may become so, and whether they ever ...

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Have you ever wanted to try out simple coding? Want to understand the basics of what it entails? This course introduces you to the skills, concepts and jargon of coding.

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Machine Learning Introduction for Everyone

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Machine Learning Introduction for Everyone

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Machine Learning Introduction for Everyone

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Offered by IBM. This three-module course introduces machine learning and data science for everyone with a foundational understanding of ... Enroll for free.

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W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.

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Welcome to Learn HTML! #

Welcome to HTML! HyperText Markup Language, or HTML, is the backbone of the web, providing the content, as well as the structure of that content, that you see displayed in your web browser.

Unless you're reading a PDF or a printed version of this page, this content is made up of various HTML elements and text. HTML is the content layer of the web. HTML elements are the nodes that make up the Document Object Model.

Cascading Style Sheets provide the look and feel, or presentation layer of the page. JavaScript is the behavior layer, often used to manipulate the objects within a document. Sites that are built with JavaScript frameworks are really just manipulating HTML. In turn, it's important to mark up your HTML in a way that scripts can easily parse and that assistive technologies can easily understand. This means writing HTML code with modern standards.

This HTML course for web developers provides a solid overview for developers, from novice to expert level HTML. If you're completely new to HTML, you will learn how to build structurally sound content. If you've been building websites for years, this course may fill in gaps in knowledge that you didn't even know you had.

Along this journey, we will be building the structure for MachineLearningWorkshop.com. No machines were harmed in the creation of this series.

This is not a complete reference. Each section introduces the section topic with brief explanations and examples, providing you an opportunity to explore further. There will be links to topic references, such as MDN and WHATWG specifications, and other web.dev articles. While this is not an accessibility course, each section will include accessibility best practices and specific issues, with links to deeper dives on the topic. Each section will have a short assessment to help people confirm their understanding.

Here's what you'll learn:

A brief introduction to the key concepts in HTML.

Learn how to structure your HTML documents with a solid foundation.

How to use meta tags to provide information about your documents.

Using the correct HTML elements to describe your document content.

How to correctly use sectioning elements to give meaning to your content.

Learn about the different global attributes along with attributes specific to particular HTML elements.

How to format text using HTML.

Everything you need to know about links.

Lists and other ways of grouping your content.

Navigation is a key element of any site of application, and it starts with HTML.

Understanding how to use tables to mark up tabular data.

An overview of forms in HTML.

An overview of images in HTML.

Discover how to work with HTML media such as audio and video.

An explanation of template, slot, and shadow.

Learn how HTML information can be exposed and manipulated using JavaScript.

How to manage focus order in your HTML documents.

An introduction to the range of elements used to mark-up text.

Discover how the very useful details and summary elements work, and where to use them.

The &LTdialog> element is a useful element for representing any kind of dialog in HTML, find out how it works.

Wrapping up with some further resources.

So, are you ready to learn HTML? Let's get started.

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Welcome to HTML! HyperText Markup Language, or HTML, is the backbone of the web, providing the content, as well as the structure of that content, that you see displayed in your web browser. Unless you're reading a PDF or a printed version of this page, this content is made up of various HTML elements and text.

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An engaged software development community

Codewars is a collective effort by its users. They are creators-authoring kata to teach various techniques, solving kata with solutions that enlighten others, and commenting with constructive feedback.

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Codewars - Achieve mastery through coding practice and developer mentorship

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A coding practice website for all programming levels - Join a community of over 3 million developers and improve your coding skills in over 55 programming languages!