Introduction to Conversational Interfaces

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Learn how and why conversational interfaces have developed and the ways in which this exciting new technology is evolving.

How do you know if you’re talking to a human or a machine?

From corporate chatbots to our everyday interactions with Alexa and Siri, conversational interfaces (where a computer can reply to instructions using natural language) have become increasingly commonplace – and hard to spot.

On this course, you’ll get an introduction to conversational and voice-based interfaces, discovering how they developed and what people working in this field think about them.

You’ll explore how they can be designed and developed effectively and responsibly, and learn about the skills and design thinking you need to develop a successful conversational interface.

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Robotics With Raspberry Pi: Build and Program Your First Robot Buggy

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Build and program your first robot buggy using a Raspberry Pi, learning how to connect motors, add sensors and write algorithms.

Learn robotics by building a robot buggy and controlling it with a Raspberry Pi

On this course from the Raspberry Pi Foundation, you’ll build a robot buggy controlled by a Raspberry Pi.

You’ll start by learning how to connect motors to your Raspberry Pi, and how to write a Python program to control them to move your buggy. You’ll move on to adding sensors to your robot and writing algorithms that use the inputs from these sensors, giving your robot the ability to follow lines and avoid obstacles.

You’ll examine the wider context of modern robotics, and think about how robotics affects society.

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Python in High Performance Computing

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Learn how to analyse Python programmes and identify performance barriers to help you work more efficiently.

Speed up Python programs using optimisation and parallelisation techniques

The Python programming language is popular in scientific computing because of the benefits it offers for fast code development. The performance of pure Python programs is often suboptimal, but there are ways to make them faster and more efficient.

On this course, you’ll find out how to identify performance bottlenecks, perform numerical computations efficiently, and extend Python with compiled code. You’ll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high performance computing.

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Artificial Intelligence: Distinguishing Between Fact and Fiction

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How well do you understand artificial intelligence (AI)? Explore how to separate the reality from the hype on this course.

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

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Applications of TinyML

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Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

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Do you know what happens when you say “OK Google” to a Google device? Is your Google Home always listening?

Following on the Foundations of Tiny ML course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.

Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind “OK Google,” “Alexa,” and smartphone features on Android and Apple . Learn about real-word industry applications of TinyML as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.

Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in the TinyML Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.

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Fundamentals of TinyML

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Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

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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.

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Data Structures: An Active Learning Approach

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This interactive text used in this course was written with the intention of teaching Computer Science students about various data structures as well as the applications in which each data structure would be appropriate to use. It is currently being taught at the University of California, San Diego (UCSD), the University of San Diego (USD), and the University of Puerto Rico (UPR).
 
This coursework utilizes the Active Learning approach to instruction, meaning it has various activities embedded throughout to help stimulate your learning and improve your understanding of the materials we will cover. You will encounter "STOP and Think" questions that will help you reflect on the material, "Exercise Breaks" that will test your knowledge and understanding of the concepts discussed, and "Code Challenges" that will allow you to actually implement some of the algorithms we will cover.
 
Currently, all code challenges are in C++ or Python, but the vast majority of the content is language-agnostic theory of complexity and algorithm analysis. In other words, even without C++ or Python knowledge, the key takeaways can still be obtained.

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Python Project: pillow, tesseract, and opencv

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This course will walk you through a hands-on project suitable for a portfolio. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognize text (tesseract and py-tesseract), and how to identify faces in images using the popular opencv library. By the end of the course you will have worked with three different libraries available for Python 3 to create a real-world data-analysis project.

The course is best-suited for learners who have taken the first four courses of the Python 3 Programming Specialization. Learners who already have Python programming skills but want to practice with a hands-on, real-world data-analysis project can also benefit from this course. This is the fifth and final course in the Python 3 Programming Specialization

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Introduction to Clinical Data

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This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.

The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content

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Cloud Computing Concepts, Part 1

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Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing.

Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia

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