There is one session available:
Google AI for Anyone
About this courseSkip About this course
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.
At a glance
- Language: English
- Video Transcript: English
- Associated programs:
- Professional Certificate in Fundamentals of Google AI for Web Based Machine Learning
- Associated skills: Artificial Intelligence, Computer Science, Basic Math, Artificial Neural Networks, Computer Vision, Machine Learning, Recommender Systems
What you'll learnSkip What you'll learn
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