What you'll learn
What Artificial Intelligence is, and what it is not.
What types of supposedly intelligent systems are not AI systems.
How Machine Learning is different to the classical software development approach.
A solid understanding of the difference between AI, Machine Learning and Deep Learning
A solid understanding of the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
Function Approximators and the role of Neural Networks as Universal Function Approximators.
Encoding and Decoding to work with non-numeric, categorical type data.
An intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
Requirements
High school Math and a deep interest in machine learning would be the only requirements for this series of lessons. There is no coding or complex mathematics involved in this course. If you can't remember your high-school Math, it will not prevent you from learning the concepts in this course. But an appreciation for the importance of Mathematics in Machine will be an important mindset to have.
Description
This course provides the essential foundations for any beginner who truly wants to master AI and machine learning. Mastering any craft, requires that you have solid foundations. Anyone who is thinking about starting a career in AI and machine learning, will benefit from this. Non-technical professionals such as marketers, business analysts etc. will be able to effectively converse and work with data scientists, machine learning engineers or even data scientists, if they apply themselves to understanding the concepts in this course.Many misconceptions about artificial intelligence and machine learning are clarified in this course. After completing this course, you will understand the difference between AI, machine learning, deep learning, reinforcement learning, deep reinforcement learning, etc.The fundamental concepts that govern how machines learn, and the ways in machine-learning use mathematics in the background, are clearly explained. I only reference high school math concepts in this course. This is because neural networks, which are used extensively in all spheres of machine learning, are mathematical function approximators. I therefore cover the basics of functions, and how functions can be approximated, as part of the explanation of neural networks.If you hate mathematics, then either you will hate this course... or this course might help you to see mathematics differently.This course does not get into any coding, or complex mathematics. This course is intended to be a baseline steppingstone for more advanced courses in AI and machine learning.
Overview
Section 1: Introduction
Lecture 1 Introduction and Course Outline
Lecture 2 What is Artificial Intelligence? How intelligent is AI really?
Lecture 3 Why modern-day intelligent systems are NOT necessarily AI systems?
Lecture 4 What is Machine Learning - Part 1
Lecture 5 What is Machine Learning - Part 2
Lecture 6 The 3 Main Machine Learning Techniques
Lecture 7 The Basics of Deep Learning and Neural Networks
This course is for absolute beginners who are looking for the best beginner's guided to artificial intelligence and machine learning.,If you hold a professional, but non-technical position, such as a Business Analyst or Marketer, this course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists.,Alternatively, if you do have a very basic knowledge of artificial intelligence and machine learning, this course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood.,If you have an aversion or intense dislike for Mathematics, then this course, and machine learning in general, is not for you.,If you are looking for coding tips, technical detail about the different machine learning algorithms, back-propagation in Neural Networks, loss functions, gradient descent, policy gradient methods, etc., then these series of lessons are definitely not for you.


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