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The Ultimate Beginner's Guide to AI and Machine Learning (Udemy.com)

Understand AI, machine learning and neural networks through clear foundations, examples and practical demonstrations.

4.3
2,693 reviews

Created by: Irlon Terblanche

Last updated February 2026

Our take

Based on the ratings of 2,693 students, a sample of their written reviews and the syllabus, as the course stood in February 2026. No course pays to be reviewed.

Irlon Terblanche's course is a very large beginner collection on AI and machine learning, 36 hours across 639 lectures. The opening sections cover what AI is, supervised and unsupervised learning, and how a single neuron works out its output from weights, bias and ReLU. After that it widens into recommendation systems, a Microsoft Model Builder demo (Windows and Visual Studio are only needed to reproduce it), text embeddings, recorded webinars and a long run of business topics. There are no coding exercises. It suits managers and curious beginners more than people planning to build models.

Most of the 2,693 ratings are positive, with 1,277 five-star and 1,021 four-star. Happy reviewers call the explanations clear and straightforward, and one calls the neural network teaching brilliant. The complaints cluster later in the course. Lower-star reviewers describe the same points coming round again and again, quizzes with duplicate questions, robotic narration, stock footage that has nothing to do with the talk, and a drift toward marketing. One reviewer puts the useful teaching at about 8 hours. Others say it already feels dated, since it references ChatGPT from 2023. One more reports a playback glitch at the end of segments.

Value is the real question here. The course was last updated in February 2026, yet reviewers still call parts of it dated. Treat sections 1 to 4 as the core, add the Model Builder demo to watch or, on Windows, to follow along, and treat everything else as optional browsing. Used that way, it works as a gentle entry point for a non-technical person. Anyone wanting depth, a project or real code should look elsewhere, because the later material is mostly overview.

Best forNon-technical managers who want plain-English AI terms and neural network basics, and are happy to stop after the early sections.
Skip it ifYou want hands-on coding projects or 36 hours of fresh material, since reviewers report heavy repetition later on.

Pros

  • Early sections explain neurons, weights, bias and ReLU with simple worked calculations
  • Reviewers call the core explanations clear, straightforward and easy to follow
  • Covers supervised, unsupervised and reinforcement learning in plain language
  • Business examples and a Model Builder demo suit managers who talk to technical teams

Cons

  • Reviewers report long stretches of repeated points and duplicate quiz questions
  • Some narration and visuals feel AI-generated, with stock footage unrelated to the talk
  • Parts feel dated, and there are no coding exercises or build-it-yourself projects
Just absolutely brilliant explanation of the Neural Networks
Anonymized User, a student

Beginner is accurate for sections 1 to 4. Later code walkthroughs assume some programming, algebra or statistics.

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What you will learn

  • Explain what artificial intelligence (AI) is and is not, and recognise common misconceptions about intelligent systems.
  • Distinguish AI, machine learning and deep learning, and explain how they relate.
  • Compare traditional rule-based programming with machine learning that learns relationships from examples.
  • Distinguish learning algorithms, trained models, features and labels using everyday examples.
  • Compare supervised, unsupervised and reinforcement learning, and identify suitable example problems for each.
  • Distinguish classification from regression and interpret a simple numerical prediction.
  • Explain how encoding and decoding let machine-learning models work with non-numeric, categorical data.
  • Explain function approximators and the idea of neural networks as universal function approximators.
  • Describe neurons, layers, network width and depth, and how simple calculations combine in a neural network.
  • Calculate a simple neuron's output using inputs, weights, bias and ReLU activation.
  • Distinguish choosing a network's structure, training its parameters and using it to make predictions.
  • Explain reinforcement-learning agents, environments, actions and rewards, including why reward design matters.
  • Describe clustering and dimensionality reduction, with introductory examples of K-means and PCA.
  • Compare recommendation-system approaches and explain challenges such as cold starts and limited user data.
  • Explain the data preparation, training and evaluation stages demonstrated with Microsoft Model Builder in Visual Studio.
  • Explain why fitting training data well is insufficient, and why overfitting and evaluation on new data matter.
  • Describe text embeddings and their role in representing meaning for natural language processing (NLP).
  • Outline the roles of convolutional networks (CNNs), recurrent networks (RNNs) and transformers at a conceptual level.
  • Describe generative AI, large language models (LLMs) and GANs at an introductory level, including their limitations.
  • Recognise why convincing AI output still needs checking, using examples of model capabilities, errors and prompting.
  • Identify potential AI uses in marketing, customer experience, HR, finance and operations from the business examples.
  • Discuss human-AI collaboration, adoption barriers and questions to ask before introducing AI into a team.
  • Discuss data quality, privacy, bias, ethics and governance considerations when evaluating AI applications.
  • Use AI and machine-learning terminology more precisely in conversations with data scientists and technical colleagues.

Course content

63 sections · 639 lectures · 36 hours of video 102 quizzes, 2 practice tests, 4 assignments, 12 articles

  1. 1AI and Machine Learning for Beginners 1 free preview5 lectures · 1 quiz · 7 min
  2. 2What is Artificial Intelligence? 1 free preview2 lectures · 1 quiz · 15 min
  3. 3What is Machine Learning?3 lectures · 1 quiz · 36 min
  4. 4Deep Learning and Neural Networks4 lectures · 1 quiz · 24 min
  5. 5Artificial Intelligence Insights and Fundamentals4 lectures · 39 min
  6. 6AI Applied to Business: Recommenders in eCommerce, Loyalty and other settings.4 lectures · 38 min
  7. 7Bonus: Ecosystem.ai & X-idian Webinar- Personalized Rewards & Loyalty Programmes 1 free preview11 lectures · 1.1 hours
  8. 8Introducing the next part of this course: Practical AI with Model Builder.2 lectures · 1 quiz · 8 min
  9. 9Visual Studio and Model Builder1 lecture · 1 quiz · 12 min
  10. 10Model Builder and the Machine Learning Process 1 free preview3 lectures · 1 quiz · 19 min

Who it is for

The instructor says it suits

  • Absolute beginners who want to understand what AI and machine learning mean, how they work and where common misconceptions arise.
  • Aspiring data scientists and machine-learning engineers who want conceptual foundations before progressing to deeper mathematics and programming.
  • Business leaders, executives and managers seeking a clearer basis for discussing AI opportunities, limitations and adoption.
  • Business analysts, data analysts and other non-technical professionals who want more productive conversations with AI specialists.
  • Entrepreneurs and startup founders exploring how AI could support a product, service or business idea.
  • Product managers, project managers and team leaders who want to connect AI concepts with customer needs and human-AI collaboration.
  • Marketing, sales and customer-experience professionals interested in recommendations, personalisation and customer insights at an introductory level.
  • HR and learning professionals exploring AI's implications for recruitment, workforce skills and team development.

What you need before you start

  • No prior AI knowledge, programming experience or advanced mathematics is needed to begin the foundational lessons in Sections 1–4.
  • Basic arithmetic and a willingness to work through simple formulas are useful. The foundations explain mathematical ideas through examples and diagrams; you do not need to arrive confident in mathematics.
  • You need basic computer skills and an internet-connected device to watch the lessons and use the resources. A computer is useful for following technical demonstrations.
  • For the Model Builder demonstrations in Sections 8–11, reproducing the workflow requires a suitable Windows computer, Visual Studio and Microsoft Model Builder. You can also watch to understand the process.
  • Programming familiarity and basic algebra or statistics help with the later code walkthroughs and technical topics. These are deeper routes beyond the beginner foundations; their setup needs vary.
  • Some software demonstrations and talks were recorded using earlier versions. Interfaces and available features may differ today; focus on the underlying workflow and check current setup instructions before reproducing a demo.

How it compares

Against the other 1,182 rated Udemy courses in IT & Software on CourseDuck:

  • Rating4.3 out of 5higher than 20% of them
  • Reviews2,693#193 of 1,183 by number of reviews
  • Length36.1 hourslonger than 95% of them
  • Last updatedFebruary 2026more recently than 66% of them

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Course Description

Build a clear foundation in artificial intelligence and machine learning, even if you are starting without a technical background.

What is AI, and what is it not? How does learning from examples differ from writing rules? What does a neural network actually calculate? This course helps you work through these questions, recognise common misconceptions and use AI terminology with greater confidence.

Start with the ideas behind the technology

Explore AI, machine learning, deep learning, supervised learning, unsupervised learning and reinforcement learning. Make sense of algorithms, models, features and labels, then connect these terms to everyday examples of prediction and decision-making.

The neural-network foundations explain functions and function approximation, encoding and decoding, and the roles of weights, bias and activation functions. Work through simple neuron calculations and distinguish network structure, training and prediction. Four shorter neural-network lessons and the first four scenario-based quizzes help you check your understanding, with explanations for every answer choice.

A beginner entry point, with room to go deeper

No prior programming experience or advanced mathematics is needed to begin the foundational lessons. Everyday examples, diagrams and simple calculations help explain what the mathematics means and why it matters. You do not need to feel confident in mathematics before starting.

The wider course includes technical demonstrations, code walkthroughs, conceptual overviews and business discussions at different levels of depth. The Microsoft Model Builder sequence demonstrates an AutoML workflow in Visual Studio. Further material explores recommendation systems, text embeddings, transformers, generative AI and large language models, alongside introductory overviews of CNNs, RNNs and GANs. These architecture overviews develop conceptual understanding; they do not provide step-by-step projects for building each model.

Connect the foundations to your work

Explore examples involving marketing, customer experience, human resources, finance, operations and business strategy. Consider human-AI collaboration, data quality, ethics and adoption questions so you can have more informed conversations with technical colleagues and evaluate AI proposals more thoughtfully.

Choose a route through the course

Begin with Sections 1–4 for the foundations and Quizzes 1–4. Continue with Sections 5–6 for learning methods and recommendation systems. Choose Sections 8–11 for the Model Builder demonstrations, or Sections 12–25 for text embeddings and technical applications. Explore the recorded talks, prompting examples and wider business library according to your interests. This is a substantial collection: you can work through the foundations first and return to other topics as your needs develop.

You can watch technical demonstrations to understand the workflow, or reproduce them with the relevant software and background. Model Builder uses Windows and Visual Studio; programming familiarity helps with later code walkthroughs. Some recordings use earlier software versions, so interfaces and features may differ today.

The course combines instructor-created lessons, recorded webinars and talks, and lessons produced with AI assistance, including synthetic narration in selected sections. Together, these formats offer a foundation for further study and a broad library for exploring AI in practice.
Read More Beginner Beginner Level product length iconTime: 36.1h

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Instructor Details

Irlon Terblanche

Hi there! I'm Irlon Terblanche, a seasoned CEO, management consultant, and technologist, leveraging a rich background in AI, Bitcoin, banking, investments, and technology to revolutionise how we approach education and financial abundance for everyone. My journey began with an Electromechanical Engineering degree from the University of Cape Town, bolstered by an MBA. In parallel with maintaining C-level corporate roles, including CEO positions in the banking, investments and insurance sectors, I've honed my skills in full-stack development, machine learning, cybersecurity and Bitcoin. I am well-versed in global monetary and economic policy matters.

My passion for continuous, hands-on learning and innovation in the tech space drives my courses. I aim to simplify complex concepts, drawing from a deep and foundational understanding of concepts in engineering, financial markets, and technology. Join me to gain foundational insights and skills that will empower your journey in the evolving world of technology and finance. Let's explore the endless possibilities together!

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Reviews

4.3

2,693 ratings on Udemy

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By Tom Stanton on 3/10/2026

There is a glitch at the end of each segment that stops the speaker mid sentence and freezes the stream/content. My only way out is to advance to the next segment without knowing whether the previous segment is marked as "concluded".

By Charles Kruger on 2/15/2026

The course started of very well with very good information, but after the first section, it becomes a continuous repetition of the same information but just from a different point of new. There is not really any new information being shared after the first course completion.

By Beatriz Villar Endrino on 1/13/2026

The first part of the course is more professional and technical, what it is great, but I didn't like the recording session. The second part, from section 113 to end, it's a little bit repetitive. The evaluation tests are repetitive too, and many of the questions are equal one time and another

By patrascu mihai on 1/11/2026

it's clarifying the terms an the types of om how artificial intelligence in learning and how human interacts with it.

By Udemy User on 1/8/2026

Unfortunately there are a lot of repetition in the course, also worht mentioning: the structure is not consistent. Some really interesting Exposés like Prompting techniques have been split between several modules without any (recognizable) logic.

By lahari budhireddy on 12/27/2025

yah ! this course is very helpful to me , to improve my knowledge , strengths and confidence levels on my knowledge and also self motivation etc. Thank you

By Gilad Yefet on 11/16/2025

The course provides a good overview of the new world of artificial intelligence and lists a broad set of tools that allow you to specialize in the field.

By Migration 3 on 11/10/2025

nicely explain the concepts. rules are replaced with mathematical functions. mark & score example is very easy to connect linear regression technique

By GEORGE PAPAPANAGIS on 10/29/2025

It is the most amazing practical presentation of lecture / course, referring to AI, I have ever watched by now. Great Well Done, to the lecturer, for his great and fantastic work!!!

By Gwendolyn Mickens on 10/27/2025

I love the brilliance of the videos, they captivate your attention and the information provided via the videos is easy to understand.

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Overall Score : 86 / 100

The Ultimate Beginner's Guide to AI and Machine Learning
Visit course on Udemy Write a review
  • Video Duration: icon 36.1h
  • Price: $64.99
  • Provider: icon Udemy
  • Updated: icon February 2026
  • Level: Beginner Beginner
  • Language: icon English
  • Certificate: icon Yes
  • Lectures:639 in 63 sections
  • Practice:102 quizzes, 2 practice tests
  • Students:15,000
  • Subtitles:9 languages
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