The course on fundamentals of TinyML course focuses on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the language? of TinyML.
About this course
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 provide an introduction to TinyML and are not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.
What you will learn from Fundamentals of TinyML?
- 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
- Basic Scripting in Python
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
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- Harvard University
- Online Course
- 1-3 Months
- Free Course (Affordable Certificate)
- Artificial intelligence Data Science Data Science with 'Python' Deep learning Machine learning TinyML