- Overview
- Curriculum
- Discussion
- Review
- Instructor
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Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning.
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Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent.
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Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
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Explain the concept of machine learning and its relation to AI.
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Define artificial intelligence (AI) and differentiate it from human intelligence.
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Describe what Artificial Intelligence is, and what it is not.
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Explain what types of sophisticated software systems are not AI systems.
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Describe how Machine Learning is different to the classical software development approach.
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Compare and contrast supervised, unsupervised, and reinforcement learning.
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Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
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Explain Function Approximators and the role of Neural Networks as Universal Function Approximators.
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Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data.
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Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
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Identify examples of AI in everyday life and discuss their impact.
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Evaluate the effectiveness of different AI applications in real-world scenarios.
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Apply basic principles of neural networks to a hypothetical problem.
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Discuss the role of data in training AI models
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Construct a neural network model for a specified task
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Assess the impact of AI on job markets and skill requirements
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See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
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Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
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Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
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Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
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Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
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Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
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Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
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Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
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Learn how to use Model Builder to train models without having to code.
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There are no requirements or prerequisites for this course, but the items listed below are a guide to useful background knowledge that will increase the value and benefits of this course:
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High school Math and a deep interest in machine learning would be highly beneficial for this series of lessons. There is no coding or complex mathematics involved in this course. If you can't remember high-school math, it will not prevent you from learning the concepts in this course.
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An appreciation for, but not a deep knowledge of, the importance of Mathematics and Statistics in Machine Learning.
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Basic computer literacy, including familiarity with operating a computer.
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A basic understanding of supervised machine learning is required. The student would at the very least need to understand what regression is, what features are, and what it means for a model to be trained to fit a function to input features in order to predict labels.
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The student needs to have a Windows machine with a few GB of free disk space to install Visual Studio, in order to replicate the machine learning process I will demonstrate. However, this is not essential.
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A Windows machine is ideal, but a student with a Mac will still be able to follow along. The course content is visual enough to demonstrate the concepts, without the student having to physically do the machine learning exercise.
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Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations.
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Busy professionals who need a short, easy but solid understanding of AI fundamentals.
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Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role.
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Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration.
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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.
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Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products.
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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.
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Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood.
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This course is not for you if you have an aversion or intense dislike for Mathematics.
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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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This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
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Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
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Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
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Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.
Unlock the Future: Dive into the World of AI and ML!
Welcome to an extraordinary journey into the realms of Artificial Intelligence and Machine Learning. Led by industry expert Peter Alkema, this course is not just an educational experience; it's an adventure into the technologies shaping our future. Whether you're a curious beginner, a business leader, or an aspiring tech guru, this course promises to transform your understanding of some of the most cutting-edge topics in tech.
Why This Course?
Designed for Curiosity and Career: Tailored for both personal and professional growth, this course demystifies AI and ML, making them accessible to everyone. It's perfect for busy professionals, entrepreneurs, and anyone with a thirst for knowledge.
No Math Fears: We've designed the course to be inclusive, requiring no prior expertise in math or coding. It's all about understanding concepts in a friendly, approachable manner.
Lifetime Access and Flexible Learning: Learn at your pace with full lifetime access to all resources, including videos, articles, and downloadable materials.
What You'll Achieve:
Grasp the Core Concepts: Understand the difference between AI, ML, and Deep Learning. Learn what sets them apart and how they're revolutionizing industries.
Debunk Myths: Discover why systems like ChatGPT aren't truly intelligent and explore the limitations of current AI technologies.
Practical Skills: Gain hands-on experience with tools like Microsoft's Model Builder and ML .Net. Understand the complete machine learning process, from data preparation to model evaluation.
Real-World Applications: See how AI and ML are being applied in various sectors. Discuss their impact on job markets and skill requirements.
Course Highlights:
Engaging Video Lectures: Over 4 hours of high-quality, engaging video content that breaks down complex ideas into digestible segments.
Comprehensive Topics: From the basics of neural networks to the intricacies of supervised and unsupervised learning.
Practical Demonstrations: Learn by doing with practical exercises and demonstrations.
Dynamic Learning Resources: An article and a downloadable resource to complement your learning journey.
Mobile and PC Access: Learn on the go or from the comfort of your living room.
Course Structure:
The course is divided into 9 comprehensive sections, each designed to build upon the last, ensuring a smooth learning curve. Starting with an introduction to AI and ML, it moves through various topics like function approximation, neural networks, and deep learning, concluding with practical demonstrations of machine learning in action.
Enroll Now and Transform Your Understanding of AI and ML!
Join us on this captivating journey into AI and ML. With Peter Alkema's expert guidance, engaging content, and practical insights, you're not just learning; you're preparing for the future. Enroll today and be part of the AI revolution!
Introduction and Course Outline
Introduction and Course Outline
What is Artificial Intelligence? How intelligent is AI and ChatGPT really?
What is Artificial Intelligence? How intelligent is AI and ChatGPT really?
Traditional Software Programmes vs AI systems vs?
Traditional Software Programmes vs AI systems vs?
Math and Data Science replaces Traditional Programming. A regression example.
Math and Data Science replaces Traditional Programming. A regression example.
Introducing Function Approximation, Neural Networks, Encoding and Decoding
Introducing Function Approximation, Neural Networks, Encoding and Decoding
Supervised, Unsupervised and Reinforcement Machine Learning Models & Algorithms
Supervised, Unsupervised and Reinforcement Machine Learning Models & Algorithms
The Basics of Deep Learning and Neural Networks
The Basics of Deep Learning and Neural Networks
Introduction, Prerequisites and Learning Outcomes
Introduction, Prerequisites and Learning Outcomes
Introducing Model Builder and the Approach for this Course
Introducing Model Builder and the Approach for this Course
Download, Install and Configure Visual Studio
Download, Install and Configure Visual Studio
Launch Visual Studio and Start a Coding Project
Launch Visual Studio and Start a Coding Project
Introducing Model Builder and the Machine Learning Process
Introducing Model Builder and the Machine Learning Process
Model Builder Tasks
Model Builder Tasks
No Discussion Found
4.4
113 Reviews
Peter Alkema
Instructor
Irlon Terblanche
Instructor
This Course Includes
