Shadowing Practice: What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners - Learn English Speaking with Video
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Hello and welcome back to this beginner series on machine learning and AI.
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In this video, I'm going to give you a quick introduction to what AI and machine learning are.
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In this video, some of the things that you're going to be learning about are, what is AI and ML?
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We're also going to learn about what you can do with AI and ML, and as a .NET developer, how you can get started with AI and ML.
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All right, so first off, what is AI and ML?
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You can look at it as different layers of basically the same thing.
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And at the top level, you have AI or artificial intelligence.
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And these are typically agents that automate tasks that are typically performed by humans.
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So you can think of this as your spam filter in your email, right?
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So at the end of the day, once you set some rules as to how you want your email organized, It's some sort of autonomous agent that's performing these actions on your behalf,
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as opposed to you having to do it by yourself.
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At the next level, you have machine learning or ML.
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And these are essentially systems that use algorithms and data to infer patterns.
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Now, the reason why you have machine learning is, again, think of your email inbox.
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There's a certain point where rules become so overwhelming
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that it's really difficult to sort of make any sort of meaning out of those rules and manage that.
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So that's sort of where machine learning comes in.
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And again, using data and algorithms, it's able to learn what those rules are and infer those patterns.
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A level lower here, you have deep learning.
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It's a machine learning technique that uses specialized algorithms known as neural networks.
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And then last but not least, you have generative AI, such as the one used in models like GPT and ChatGPT.
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And essentially this uses deep learning algorithms, typically known as transformers, to generate new content like text, images, and other content.
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So what can you do with AI and ML?
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At a high level, you can essentially extract key insights and summarize information with AI and ML.
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Some examples of that are language.
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So you could put, for example, analyze the sentiment of customer reviews.
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You can build virtual agents or chatbots, like the one pictured here, Bing Chat.
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You can translate text or code, and you can even use these models to explain code.
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When it comes to computer vision, you can use these models to identify objects in an image or a video.
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Similarly, for classification, if you need to tag customer reported issues with some severity level, you can leverage machine learning for that and AI.
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You can also do anomaly detection, tasks like predictive maintenance, when a certain piece of equipment is going to fail.
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Last but not least, you have forecasting.
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So if you're looking at quarterly sales or some sort of target that you're looking to forecast or number, you can use AI and ML to do that.
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Now, this is not a complete list, but this is just a relatively good overview of the things that you can do with AI and ML.
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So now that you know what AI and ML is and what you can do with it, how can you get started as a.NET developer?
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There's a few ways and we're going to go over a few of these throughout this entire series.
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So first and foremost, you have services such as Azure OpenAI for working with generative AI models,
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and then you have Azure Conduct Services or AI services.
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What these allow you to do is they allow you to perform task-specific tasks and workflows using the power of AI.
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You also have frameworks and libraries like Symantec Kernel for working with generative AI models.
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You have ML.NET for training custom machine learning models.
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You have libraries like AutoML, Torch Sharp, and SciSharp, which help you train custom machine learning and deep learning models.
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You have the Open Neural Network Exchange or ONNX, which helps you when inferencing and bringing models trained in other platforms over to .NET,
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and you have other libraries like plali.net, which help you with graphing and visualizing your data.
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Then last but not least, there's a series of tools to help you with these workflows, such as ModelBuilder and the ML.NET CLI, when it comes to training custom machine learning models or deep learning models.
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Then there's Polyglot Notebooks, which help with that experimentation process, and just writing code in general.
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That brings to the end of this video, and let's recap what we learned here.
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We learned what AI NML is.
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We learned the things that you can do with AI NML.
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And then last but not least, we wrapped it up with how you as a .NET developer can get started building AI applications.
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Thanks for watching.
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See you in the next video.
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Vocabulary and speaking notes for this lesson
This video has 52 sentences and 835 words to shadow. The speech runs for 4:19. The speaker talks fast, about 193 words per minute, so expect linked and reduced sounds. 83% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| algorithm noun | /ˈælɡəɹɪðm̩/ | A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input… |
| custom noun | /ˈkʌstəm/ | Frequent repetition of the same behavior; way of behavior common to many; ordinary manner; habitual practice; method of doing, living or behaving. |
| essentially adverb | /ɪˈsɛnʃəli/ | In essence: by nature. |
| developer noun | /dɪˈvɛləpɚ/ | A person or entity engaged in the creation or improvement of certain classes of products. |
| forecast noun | /ˈfɔːkɑːst/ | An estimation of a future condition. |
| neural adjective | /ˈnʊɹəl/ | Of, or relating to the nerves, neurons or the nervous system. |
| technique noun | /tɛkˈniːk/ | The practical aspects of a given art, occupation etc.; formal requirements. |
| behalf noun | /bɪˈhæf/ | The interest, benefit, or wellbeing of someone or something. |
| layer noun | /ˈleɪ̯ɚ/ | A single thickness of some material covering a surface. |
| virtual adjective | /ˈvɜɹt͡ʃuəl/ | In effect or essence, rather than in fact or reality; also, imitated, simulated. |
| similarly adverb | /ˈsɪm.ə.lə.li/ | In a like style or manner. |
| framework noun | /ˈfɹeɪm.wɜːk/ | A support structure comprising joined parts or conglomerated particles and intervening open spaces of similar or larger size. |
| filter noun | /ˈfɪltɚ/ | A device which separates a suspended, dissolved, or particulate matter from a fluid, solution, or other substance; any device that separates one substance from… |
| artificial adjective | /ˌɑː.tɪˈfɪʃ.əl/ | Man-made; made by humans; of artifice. |
| generate verb | /ˈd͡ʒɛn.ə.ɹeɪt/ | To bring into being; give rise to. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| go over verb | To look at carefully; to scrutinize; to analyze. |
Pronunciation to watch
The speaker uses 6 contractions and reduced forms, such as you're, we're, I'm. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/, detection /dɪˈtɛk.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/
- Long words — get the stress right: essentially /ɪˈsɛnʃəli/, developer /dɪˈvɛləpɚ/, similarly /ˈsɪm.ə.lə.li/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like algorithm, custom, essentially.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.