Practical Guide to AI & ML: Mastering Future Tech Skills
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Unclaimed ProfileAspiring professionals and tech enthusiasts looking to demystify artificial intelligence and machine learning will find a clear, structured roadmap in this comprehensive guide by Peter Alkema. Far from just theoretical jargon, this course is designed to transition learners from passive observers to active participants in the modern AI revolution. You will explore the foundational differences between AI, machine learning, and deep learning, while gaining a realistic perspective on how large language models function. By examining supervised, unsupervised, and reinforcement learning, the curriculum bridges the gap between complex algorithmic theory and practical, real-world utility. Students will dive deep into visual tools like Microsoft's Model Builder and ML.NET to witness end-to-end model training without needing an extensive coding background. Perfect for career changers, non-technical managers, and forward-thinking professionals, this course cultivates an essential tech mindset. You will learn to evaluate data quality, understand model evaluation metrics, and recognize how automated decision-making impacts global industries and the future job market. Empower your career by mastering these vital technological skills.
About the creator
Peter Alkema
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Peter Alkema is a highly versatile educator and business professional who bridges the gap between creative design, emerging technologies, and strategic business leadership. With a robust background in driving organizational change and digital transformation, Peter has structured his courses to help professionals, entrepreneurs, and creatives navigate the rapidly shifting modern workplace. His instructional catalog spans a diverse array of critical modern skills, ranging from hands-on creative suites like Adobe Photoshop, Illustrator, and Premiere Pro to advanced AI workflows featuring ChatGPT and Midjourney. Peter’s pedagogical approach is rooted in practical application and structural clarity, ensuring that complex theories—such as design thinking,…Show more
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Program Overview
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Learning format
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What You'll 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
- See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
- Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
- Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
- Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
- Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
- Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
- Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
- 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.
- Learn how to use Model Builder to train models without having to code.
Best For
- Non-technical managers and business leaders seeking to understand AI strategy
- Career changers aiming to enter the tech industry with a solid foundational knowledge
- Tech enthusiasts who want to demystify machine learning without deep coding prerequisites
- Professionals looking for a practical overview of how AI impacts modern workflows
Not For
- Individuals who require academic-level mathematical proofs and statistical theory
- Experienced data scientists or machine learning engineers seeking advanced algorithmic development
- Learners searching for a coding-intensive course focused on Python or R programming
- Experts looking for deep dives into specialized research-grade AI architectures
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