The Ultimate Beginner's Guide to AI and Machine Learning

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 how machine learning uses 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.

This course does not get into any coding, or complex mathematics. This course is intended to be a baseline stepping stone for more advanced courses in AI and machine learning.

What You Will Learn!

• Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning.
• Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent.
• Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
• Explain the concept of machine learning and its relation to AI.
• Define artificial intelligence (AI) and differentiate it from human intelligence.
• Describe what Artificial Intelligence is, and what it is not.
• Explain what types of sophisticated software systems are not AI systems.
• Describe how Machine Learning is different to the classical software development approach.
• Compare and contrast supervised, unsupervised, and reinforcement learning.
• Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
• Explain Function Approximators and the role of Neural Networks as Universal Function Approximators.
• Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data.
• Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
• Identify examples of AI in everyday life and discuss their impact.
• Evaluate the effectiveness of different AI applications in real-world scenarios.
• Apply basic principles of neural networks to a hypothetical problem.
• Discuss the role of data in training AI models
• Construct a neural network model for a specified task
• Assess the impact of AI on job markets and skill requirements

Who Should Attend!

• Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations.
• Busy professionals who need a short, easy but solid understanding of AI fundamentals.
• Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role.
• Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration.
• Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning.
• Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products.
• Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists.
• Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood.
• This course is not for you if you have an aversion or intense dislike for Mathematics.
• Also, 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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