シャドーイング練習: Here's the AI Roadmap You Actually Need - 動画で英語スピーキングを学ぶ

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Learning AI isn't a straight path anymore.

Actually, it's old school RPG themed.

Everybody starts off on tutorial island.

Those are like the basics of AI models, prompting, and using generalized AI tools.

But then the map opens up and you get to choose your class.

There is the strategist, the rogue, alchemist, crafter, sage, and machinist.

So by the end of this video, you will have the full map, the skill tree for your class, quests worth doing, and achievements worth unlocking.

Plus I will include a prompt that helps you determine your class

and the right quest log for you based upon your learning style and your goals.

Sounds good?

Let's go!

A portion of this video is sponsored by Crusoe.

All right, so everybody starts off on Tutorial Island in which everybody picks up a starter pack.

These are skills that you need regardless of what class that you choose.

Here is the skills tree.

The first skill is to understand generative AI, the basics of it.

What is it and what can it do?

Next, you need to learn how to prompt correctly because this is the way that you talk to these AIs.

Because if you can't communicate, how are you going to get it to do what you want?

Then we need to learn the general tools.

Tools that everybody should know how to use like chat, deep research, image video audio generation, and how to incorporate the right data.

This allows you to integrate AI into your day -to -day life.

So the good news is that there are a lot of quests, resources to choose from.

The combo that I first did like many moons ago was the Google Essentials combo.

These are two courses on Coursera, the AI Essentials course and the Prompting Essentials course.

These two courses cover all the skills for you to get your starter pack.

Very popular, sub 10 hours.

You can probably get this done just during a weekend.

The learning style is video -based with some assignments and interactive components to it, but honestly, mostly just video.

And it takes less than 10 hours to cover both courses.

And price -wise, it requires a Coursera sub for around $49 per month, but you do get a seven -day trial.

So I quite enjoy taking these courses.

I even made videos summarizing them.

However, more recently, Google has come up with a new certificate program called the Google AI Professional Certificate.

And I think if I had this option, I would take this instead.

It currently has eight different courses, going from AI fundamentals to brainstorming, planning, research and insights, writing, content creation, data analysis, app building and app deployment.

So the breadth is much wider.

It is primarily still video based, but I think it's more interactive because it has more activities sprinkled throughout the certificate.

It would take you around 13 hours to complete this.

And price -wise, it's still around $49 per month using Corsair Plus with a 7 -day free trial.

So yeah, personally, if I were to do this today, I would go with this certificate as opposed to the combo from earlier.

But with the caveat that this is a lot more Google -flavored.

Like, they focus a lot more on Google ecosystem tools, while the Google's Essential combo was pretty tool -agnostic.

So just something to keep in mind.

Another quest path that you can take

if you're into the video plus some exercises style of learning is the Anju Ning path, aka deeplearning .ai.

As many of you guys know, Andrew Ning is like a superstar AI educator amongst many other things that he does.

And the two courses

that will cover the starter pack skills would be Gen AI for everyone followed by AI prompting for everyone.

Take you about 12 to 15 hours to cover both courses.

And price -wise, it's actually free to audit all of the videos.

But if you want to have access to the labs, quizzes, and certificate, you need to pay for a deep learning AI pro,

which is around 25 to 30 dollars per month.

I would say pick this path if you want something that's more tool agnostic

and more not theoretical but more about like frameworks

and like big pictures because Andrew is just so knowledgeable and experienced in this field.

He's able to give you insights about the industry

and the specific skills in a way that I would say is pretty unmatched

but he does have a pretty specific way of teaching.

So if you're interested in this path I would definitely recommend checking out some of his videos

and teaching style first before committing.

By the way just to be super clear I'm presenting a lot of different options for you

so you can make your best choice based upon your budget,

your time, and your learning style but you don't need to be doing like multiple different paths.

You just need to choose one and you may get all the foundational skills that you need.

Okay so the next choice you have is called the Elements of AI by the University of Helsinki.

The format of this course is interactive text with exercises.

Also very popular.

It takes around 30 to 50 hours to complete so it is a big course.

It's also free and includes certificate as well.

Great option if you're looking for something very thorough, free, and you're more into the interactive text -based learning style.

Quest path number five is the Anthropic Claude Path.

Anthropic has a series of courses that covers all the skills needed for the starter pack, including CLOD 101, AI Influency,

Framework, and Foundations, AI Capabilities and Limitations, as well as Prompting Docs.

By the way, I'll be linking all the course links in the free resource in the description.

So no need to like try to write down all the course names.

Anyways, yes.

So the format is interactive, structured lessons with quizzes.

Probably take you around 10 to 15 hours.

It's also completely free and you get a badge per course as well.

However, the caveat here is that because is produced by Anthropic.

Naturally, the course heavily flavors Clod and Anthropic products.

So having done these courses, they do teach you like really great foundations as well,

but obviously they'll be teaching you based upon like the Clod stack.

And of course, we also have to talk about Clod's biggest competitor, which is OpenAI.

OpenAI also has some really great resources, specifically their AI Foundations course and the Applied AI Foundations course.

These two courses will cover all the skills you need for the starter pack.

It's also a self -paced exercise and assessment based content is great obviously also has a very strong bias towards OpenAI products, which makes sense.

So I would recommend this path only to people who are very into the OpenAI ecosystem.

It is also completely free.

You also get a badge per course.

And if you do those two courses, plus there's a third one called Agents and Workflows, you would also get a certificate.

Time will also take you around two to five hours to complete.

So shorter than Deanthropic courses.

I also got to say that the vibe of Deanthropic courses with the OpenAI courses are really, really different.

Now, of course, there are a lot of other courses and options.

I just try to pick what I think is the most comprehensive,

but also give you guys like different options based upon learning style and budget and things like that, right?

So I'm going to put on screen some of the other options that you can check out as well.

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And there are two other options I want to do a quick shout out.

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The first one is the Kaggle five -day

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Gen AI intensive with Google.

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This was a live and recorded cohort with different project assignments and very community based.

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I really love this because it's like a five -day intensive situation in which you cover all the basics.

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And there's a capstone project in the end too.

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It's also completely free.

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You get a Kaggle batch too.

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However, they only did this one time.

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It seems like they're probably not going to do it live again, but you can't access all of the recordings and go through it.

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So if you're someone who prefers cohort -based intensive live sessions, I would recommend checking this out.

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Although it's no longer live and maintained, sadly.

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The other path that I want to give a shout out is the Careers Essential in Genitive AI by Microsoft and LinkedIn.

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This is a very, very popular course back in 2024.

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It's video -based plus a final exam and Microsoft -flavored.

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Takes around six hours to complete and also free.

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However, it is quite outdated.

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I went on their landing page and it says that they're going to be updating it in September 25th, 2026.

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I filmed me this video prior to that so I haven't been able to check out this new updated version yet.

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But by the time that you see this video it will definitely be after 25th

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so if they actually release this course I would recommend checking it out especially if you like

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Microsoft flavor things.

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All right here is the summary slide again with all of the different options

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that I talked about and ones that I didn't explicitly mention too.

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I will also include a prompt in the description

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which you can copy paste into your favorite AI chatbot to help you figure out

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which quest path is the best for you as well.

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Hopefully that's helpful too because there's just like so many resource is an option.

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It's good that there are so many options, but you know, paradox of choice.

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All right, amazing.

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So at this point, if you complete one of these quest paths, you would have gotten your starter kit and you're ready to leave tutorial island.

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Now you get to make the very exciting choice of which AI class you want to belong to and level up in.

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There is the strategist, rogue, alchemist, crafter, sage, and machinist.

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I will go through each of these classes, their skills tree, and the quest paths, the resources that you can choose from.

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the first class I will cover is the strategist the strategists are the people who are interested in AI leadership

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and strategy your goal is to understand AI in a way

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that leads adoption and sets the AI direction in your organization you are the AI thought leader

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and your skill tree after getting the starter pack of course

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starts off with understanding the vendor landscape like Google Microsoft AWS OpenAI Anthropic

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so many different options what do they actually offer and What is the most suitable for you, your business, and your clients?

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You also need to acquire the skill of how to run a team with AI.

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And this is actually very non -trivial.

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You can't just give your colleagues, your employees, the people you work with AI tools and just be like, hey, let you guys figure it out.

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That doesn't work.

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You need to know how to create human AI agent teams.

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What are the delegation norms?

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How do you evaluate them?

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You also need to learn the skill to set strategy.

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Where is it that AI actually matters?

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What's the AI roadmap for your team?

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And of course, you need to lead this adoption, the execution part.

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How do you get buy -in from everybody you work with?

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Your investors and how do you actually get your team to adopt your AI roadmap and strategy.

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Doing this while also balancing, of course, business metrics like ROI.

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Now let's talk about some quests, some paths to choose from.

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Now, if you're someone who isn't already like deeply affiliated with a specific vendor

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and someone who is not necessarily really into certificates, like official certificates either, I would actually recommend the AI Leadership course by OpenAI.

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It is a self -paced, text -based, interactive course with different activities and an assessment.

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And I think it actually really does a good job of covering what AI leadership looks like

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and different considerations and frameworks that you can apply, including business priorities, governance, stakeholders, how to come up with an adoption roadmap,

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and how to draft an initial AI strategy.

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And it does this in a pretty objective way, regardless of the type of business that you have and the type of tools that you're using.

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It'll take you around three to four hours to complete, and it is free.

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And after the AI leadership course, you can also supplement it with a very, very popular course called AI for Everyone from Anjone.

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I believe this is literally like the most enrolled AI course ever.

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Takes around seven hours and it's free to audit.

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But the catch on why I say this is optional is

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because this course was actually created prior to the Gen AI boom.

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So a lot of things are pretty outdated, although a lot of the organization and team building frameworks that Andrew gives is evergreen.

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Great, so that's the path I would recommend if you're not already like deeply affiliated with a specific vendor.

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But if you are, no worries, because most of the major AI vendors do have their own version of an AI leadership coursework certificate.

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Anthropik does, Google does, Microsoft, AWS,

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OpenAI, etc. So my suggestion is to just go learn from that specific vendor and get an official certificate if you wish.

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I'm going to put on screen now some of the different options by vendor.

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There are also a bunch of university certificates

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and degrees for AI leadership as well and I'll list some of the cohort -based ones too.

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Finally, books.

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There are two books that I would recommend that you check out if you're into reading books.

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The first one is called Co -Intelligence by Ethan Malik.

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Gives a lot of frameworks for organizational thinking.

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And AI Snake Oil by Arvin Narayanan and Sayesh Kapoor.

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This is like from the skeptical perspective about what AI can't do in organizations.

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So it's pretty refreshing.

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All right, I'm gonna put on screen now a summary slide now for the strategist class.

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And with that, let's move on to the rogue class.

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The rogue class is all about using AI agents really well and hacking things together.

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Getting your agents to do your work for you.

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If you're someone who's all about hacking and tinkering and speed and learning by doing, then you are probably in the road class.

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Like creating your own custom Hermes setups, co -work setups.

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By the way, co -work is just called Claude now, I think.

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And your own agents and workflows.

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And here is your skill tree.

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First skill is knowing what agents can do.

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What's the difference between the agent versus a chatbot?

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When do you trust them?

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And when do they fail?

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The next skill is delegating your real work to AI agents.

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Testing it out, pushing the boundaries, see what they can do.

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Over days and weeks, you also have to learn how to make them repeatable.

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The amazing agentic workflows that you make, you want to make sure that you can actually use them on a day -to -day basis.

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And finally, building your own AI agent setups.

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Learning things like a Hermes setup that runs your entire life and your work.

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A core setup that helps you with content.

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And all sorts of different custom agents and workflows that help you throughout your life and work.

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Along the way, you will learn things about gateways, memory, skills, security, and how to self -host different models too.

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Now, since the road class is all about hacking things together and figuring out your own custom agent builds,

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there isn't really like a general course that will teach you all of these skills.

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You learn them as you hack along.

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So the first task of the Rogue class is to figure out

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which system or tool or ecosystem you want to start building with.

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It's all about mastering how to use AI agents

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and chaining them together in a way so that you can do what you want it to do.

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And there are a lot of different AI agents to play with.

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You have Cloud Cowork agents, now called just

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Cloud from Anthropic.

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You can use agents and build multi -agent workflows with OpenAI, Microsoft and Copilot flavored, Hermes and OpenClaw

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if you don't want to be vendor locked and if you're into using open source AI.

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So the first thing to do is just choose

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which kind of ecosystem that you're most attracted to

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and go through those specific documentations and tutorials so you learn how to use those agents and build with them.

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I'm going to put on screen now some of the different options and what each ecosystem slash tool specializes in.

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And I want to mention there is one cohort -based course

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that seems to have pretty good reviews that teaches you how to use agents as well.

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I have not taken this course.

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I have not been part of this cohort.

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Never used Omarki either.

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Apparently, it's like an ecosystem tool set, but just wanted to put it out there for anybody who wants cohort -based stuff.

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There really isn't any certificates or books in the rogue class just because of the nature of it.

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It's so like hacky and custom.

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So if you're in the rogue, alchemist, sage, or machinist class, customizing and building with open source AI as well is a must in my opinion.

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But it can be such a pain dealing with the infra surrounding these open models though, which is why when the time comes,

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I highly recommend that you check out Crusoe Intelligence Foundry.

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It does two things.

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Serverless inference allows you to run the leading open models without managing any of the infrastructure.

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And serverless fine -tuning lets you customize these open models with your own data without having to own any hardware.

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Let me show you.

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So here's a data set for my notes from the AI courses that I've been taking.

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I upload it, pick an open model, and start a fine -tune. And voila!

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Just like that, I have this custom model that's trained on how I study personally

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that I can now use for all sorts of things.

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Like building a personal study app, for example.

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Customizing a model like this used to mean provisioning clusters and finding setups for hours.

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But now you can have a working model in just minutes, which you can call from any device.

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Even as small as an Arduino or a Raspberry Pi.

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And build with them too, if you like.

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There is so much that you can do.

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So do sign up now for Crusoe Intelligence Foundry.

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Because they're currently also giving away $5 in free credits.

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Link is in the description.

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Thank you so much, Crusoe, for sponsoring this portion of the video.

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Now back to the video.

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Alright, let's move on to the alchemist class.

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Now the rule class is all about using AI agents

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and hacking them together and chaining them and customizing it in the way that you want.

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The Alchemist class is about building AI agents and turning them into sustainable and reliable systems, usually as part of other business workflows.

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And this is the skills tree.

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First, you're going to learn the fundamentals of agents since we're going to be building them.

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Then you want to build your first agent.

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Learn to give it tools and knowledge.

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Orchestrate multi -agents.

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Learn about evals and reliability, which is like the other half of building AI agents,

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is that you need to be able to assess them and how to put them into production.

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How to deploy them, monitor them, calculate costs, and make sure that they play well with the other workflows that you may have in your business.

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If you wish to be and to progress in the Alchemist class,

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the first decision point is if you want to go the no -code or the code route.

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The good news is that there are a lot of tools and resources and paths that support both of these routes.

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Let me cover the no -code route first.

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Step one is choosing a foundational course that covers how AI agents work.

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The two courses that stand out the most in this category is the Google Skills Agent Fundamentals course

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and the Vanderbilt Agentic AI and AI Agents course.

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Both have really great ratings.

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I will put their stats on screen.

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Now, if you choose to go to the code route, if you're someone in the alchemist class, you will be building your own agents.

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At Lonely Octopus, we run an AI agent bootcamp ourselves.

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And during and after the program, we see people build all sorts of AI agents that support specific workflows and businesses.

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Like for example, if you're a lawyer, you can be building case -specific AI agents that help you analyze your cases.

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If you're someone in the medical field, we've seen people build agents that support triaging, handling medical paperwork.

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Apparently there's a lot of medical paperwork as well as continued education.

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Now to be able to build these workflows,

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not only do we need to have to understand how AI agents work and know how to build them,

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you also need to learn how to integrate them into existing workflows.

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Now if you choose to go the code route, you have a lot of different choices and resources.

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So many different quests to pick from.

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There is the Deep Learning AI, Agentec AI course by Andrew Ning, which is video and assignment based.

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There's Hugging Face, which really emphasizes on the open source side.

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And it's a combination of text and code, very interactive.

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There's also the Google and Kaggle cohort based five -day AI agents intensive, the Vibe Coding Edition.

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And yes, so many more.

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Next, regardless if you chose the code or the no code option,

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you have the optional quest to go deeper onto a specific platform that you want to be shipping your AI agents on.

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That can be Anthropic, OpenAI, Google, Microsoft, AWS, Langchain, and so many other open stores and third -party platforms too.

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So basically you just want to pick which ecosystem or platform

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that you want to be using and then go through their resources and start implementing everything you learn from the foundational course.

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And finally step three, required for everybody as well, is to learn about reliability.

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Reliability is something that is so underrated and the reason why

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so many companies struggle with actually incorporating AI into their existing workflows.

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So I want to have a special emphasis on this.

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There is Lang Chang Academy that goes through this, a cohort -based Maven course, and the book called AI Engineering by Chip Wen,

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I think, is fantastic as well.

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Great.

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I should now cover all the skills that you need in the Alchemist class.

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However, people who are of the Alchemist class are people who generally are working as either contractors or in existing businesses.

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So certifications can matter.

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So I'm also going to put on screen some certificates

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and also exams that you may be interested in

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if you want to get a certificate that proves that you have mastered the alchemist class.

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Now here is the summary slide for the class including some honorable mentions too.

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Amazing!

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Next up crafter class.

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Crafters are builders, specifically builders of AI native apps and products.

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If you can master the skills of a crafter, you can build all sorts of different apps,

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consumer products, b2b products, all at lightning speed with the help of AI tools and coding agents.

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If you are a crafter, you are a builder.

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And here is the skills tree.

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First of all, got to know the foundations of AI coding.

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There's a lot of names for this like vibe coding, AI assisted engineering, agentic coding.

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Basically, there's a skill about how to use AI in order to code better and faster.

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And the interesting thing is, is that you can be a crafter without actually knowing how to code.

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Interesting.

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I'll talk a little bit more about that later.

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But first, let's go through the rest of the skills.

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Then you need to pick your tool, whether that be codex, cloud code, cursor, co -pilot, lovable, bolt.

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Crafters need their tools and then they need to master their tools.

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Learning how to use that tool to direct a coding agent in a development process.

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And as you keep building, you probably also want to start incorporating AI into the products that you're building itself, kind of meta.

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And that's when you also need to learn how to build with AI, how to build using AI and also how to build with AI.

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And finally, you need to learn the skill of how to ship responsibly, how to deploy the things that you build, make sure that they're secure.

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And how do you work with a team of crafters?

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Now, unfortunately, actually quite surprisingly, there really isn't any well -established crafter AI coding foundational courses.

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Like there are some, but I don't think any of them are very, very thorough.

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And maybe it's not so surprising because the field of AI coding just moves so quickly.

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So that's what we do need to mostly rely on tool specific, vendor specific resources for learning how to be a better crafter.

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And the first decision point that you would make in the crafter class is to decide

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if you want to go no code route or decode route to pick the tool that you want to use.

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I will put their stats on screen.

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Next, regardless if you're going to code route or to no code route, everybody then needs to learn about guidelines.

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How to sustainably build, deploy, and monitor what you build.

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For that, there is a book called Beyond Vibe Coding from Coder to AI Era Developer, which I think is a pretty good book,

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but it is technical and assumes that you know how to code.

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So if you're a no code crafter, that's okay.

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You just need to skip some of those parts.

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And finally if you're someone who works in the team of coding crafters

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or someone who manages a team of crafters i would recommend

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that you check out anthropics ai native sdlk

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which stands for software development cycle playbook it is anthropic

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and clod flavored

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but it has some really great principles for how to run teams to ship as fast as anthropic does

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because they ship really really fast okay

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i'm gonna put a summary slide on screen now we'll also have more details

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and links and the prompt to help you figure out the tool that most suits you in the free video companion below

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And with that, we'll move on to the SAGE class.

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Those of the SAGE class are the ones that really understand how these AI models work.

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In fact, they are the ones that build these models as well as customize them, called fine -tuning.

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They are deeply knowledgeable and the backbone of what everybody is building on top of.

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And here is the skills tree for the SAGE class.

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First, I got a quieter skill of how AI models actually work.

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And we're not just talking about fundamentals here.

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We're talking about the technicalities too.

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How models actually work.

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What even is gradient descent and back propagation?

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You also need to learn how to train neural networks.

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Specifically, you become intimately familiar with the transformer, which is what an LLM actually is.

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A LLM is a transformer that is trained on text data at scale.

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After this, there's also the skills of fine tuning.

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How do you customize models?

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Inference and deployment.

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How do you make the models usable by other people?

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And if you are the sagiest of sages, you would also be pre -training from scratch, actually creating your own models.

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You better have a lot of money to do that though.

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So what are the quests?

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Well, first off, we need to start off with the foundations.

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You need to learn how the models work, the underlying technology, and how to implement them.

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Luckily, there are a lot of different resources and paths that you can take to do this.

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There is the Andre Kaparthi Neuro Network Zero to Hero course, where you literally build neural networks from scratch using code.

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It's also a Stanford course, an animation series from 3Blue1Brown.

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So gotta choose one of these first.

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Then step two is to pick a track, a decision point.

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Do you want to focus on fine -tuning, which is customizing models that are already existing, or building from scratch, literally making your own AI models?

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I will put on screen some of the resource options that you have for both of these tracks.

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Then regardless of which track you're on, you need to learn the skills of deployment, how to make your models usable by other people.

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Interestingly, there's actually not that many structured courses for this because it is a pretty niche skill.

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A lot of it is just like demos and documentation and just figuring it out.

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But I will link some resources to check out in the free guide below.

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And that will cover most of the skills that you need.

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However, if you are someone who cares about certification, which you probably should care about that,

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if you are in a SAGE class and not just like doing this for fun for yourself,

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I will put on screen some certifications and exams primarily from NVIDIA

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that certify you that you are a true and proper sage.

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I will put on screen now a summary of the sage class, including the resources and some honorable mentions as well.

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Then that leads us to the final class, a class that is shrouded by mystery actually, and that is the machinist class.

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The machinist class is in the intersection of hardware and software.

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They care about physical and local AI.

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The nitty -gritty how to run and scale AI on physical devices.

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I have personally been exploring this for the past few months.

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Learning how to build with hardware, learning how to run models on hardware, and how to use AI models to control hardware.

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It has been a really fun experience.

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I have a little video here that shows some of the things I've been doing.

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But if you're someone who's interested in local AI, running on different devices, having it interact with the world,

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making AI -powered robots, just combining together AI with hardware, then you may be interested in becoming a machinist.

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And here is the skill tree.

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First, you gotta learn about the basics of how AI hardware works.

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Then you actually want to start building with it by using either a specialized kit or different devices like the ESP32,

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the Raspberry Pi, which is what I use.

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And as you tinker more with that, you learn about memory and bandwidth and compute, quantization.

404

What are the different ways to run AI models on the specific type of hardware

405

that you have and how to chain together different hardware to run the models more efficiently.

406

And finally, if you really want to become the top of the machinist class, you can learn about how to scale up,

407

like how to actually run the massive AI models that companies like OpenAI and Anthropic are running?

408

What's the underlying hardware of that?

409

Like when people are talking about AI data centers,

410

you would be learning the mechanisms behind it and how they actually work and how to operate them.

411

Or you can go full on into robotics.

412

There are certain AI models and platforms that are designed specifically for physical devices.

413

And that in itself is a whole very interesting field.

414

So what are some of the resources?

415

What are some of the quest paths?

416

Well, first off, step one is to learn the basics and to start tinkering.

417

There are a few different options, but the two that kind of stand out the most to me is the first one, the machine learning systems.

418

This is two volumes of textbooks, which is completely free, including interactive labs, software, and you can buy their hardware kits too.

419

Take you probably around like 20 to 30 hours to get through all of it.

420

It is very extensive.

421

I'm actually working through it myself right now and I'm learning so much.

422

There is also an MIT course called TinyML and Efficient AI Computing.

423

This is the university track.

424

This is for you if you are someone who likes to learn in a university way.

425

It covers all the theory really, really well and includes different assignments.

426

It's about 50 hours to complete.

427

Then after this, we become more practical.

428

How do you actually get these large language models and different types of AI models to run on these devices?

429

The major player here is NVIDIA.

430

Not only can you learn this knowledge and apply them, they also do offer certificates if that is something that you care about,

431

which you probably should care about, especially if you're someone who's interested in scaling up or learning more about AI data centers,

432

how to operate them and all the underlying mechanisms or

433

if you're someone who wants to go deeper into robotics

434

and there is the Hugging Face Robotics course which you can learn

435

if you do not know anything about robotics but really want to go deep into it.

436

It is completely free and it's great if you want to crash course on robotics.

437

NVIDIA also has some great resources on robotics and physical AI.

438

Overall, I would say the Machinist class is a class that has the least amount of resources,

439

just because it's so new and is the intersection of both hardware and AI.

440

So do expect to be reading through a lot of documentation and just experimenting a lot.

441

That is what I am learning as I embark on this journey as well.

442

I'm gonna put a summary slide now of the Machina's class, all the different resources and some honorable mentions too.

443

And with that, we are at the end of this video.

444

Congratulations!

445

Let me know in the comments, what is your class?

446

And how far along are you in your learning journey?

447

For me, my major class is the alchemist class with a minor class in crafter and rogue.

448

And the machinist class is also one that I'm very, very interested in.

449

So yes, there's nothing stopping you from belonging to different classes too.

450

There's so much to learn and honestly, I'm having so much fun doing it.

451

And with that, thank you so much for watching until the end of this video.

452

I will see you guys in the next video for live stream.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「Here's the AI Roadmap You Actually Need」を教材にしています。繰り返し出てくる語は次のとおりです：agent, skill, model, learn。この動画には、シャドーイング用の文が452文、単語が5877語あります。音声の長さは25:31です。話す速さは速く、1分あたり約230語です。音のつながりや弱く発音される音が多くなります。単語の82%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
certificate 名詞	/səˈtɪf.ɪ.kɪt/	証明書, 証書
skill 名詞	/skɪl/	腕, 技
quest 名詞	/kwɛst/	探索, 探求
hardware 名詞	/ˈhɑɹdˌwɛɹ/	ハードウェア, ハード
sage 名詞	/seɪd͡ʒ/	セージ
vendor 名詞	/ˈvɛn.dɚ/	売り手, 販売者
ecosystem 名詞	/ˈikoʊˌsɪstəm/	生態系
summary 名詞	/ˈsʌm.ə.ɹi/	要約, 概要
robotics 名詞	/ɹoʊˈbɑ.tɪks/	ロボット工学, ロボティックス
custom 名詞	/ˈkʌstəm/	習慣, 風俗
slide 名詞	/ˈslaɪ̯d/	滑り台
framework 名詞	/ˈfɹeɪm.wɜːk/	骨組み
tune 名詞	/tjuːn/	曲, チューン
flavor 名詞	/ˈfleɪvə/	味, 風味
fundamental 形容詞	/ˌfʌn.dəˈmɛn.təl/	基本的な

動画に出てくる句動詞

単語	意味
check out 動詞	試験する, 検査する
pick up 動詞	拾う, 拾い上げる
write down 動詞	書き留める, 記す

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

How do you evaluate them?
How do you get buy -in from everybody you work with?
What's the difference between the agent versus a chatbot?
When do you trust them?
How do you customize models?

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
条件文 if + 節, will/would + 動詞 — 条件とその結果	if I had this option, I would take
現在完了進行形 have/has been + -ing — 以前に始まり今も続いている動作	I've been taking · have personally been exploring · I've been doing
関係詞節 who / which + 節 — 人や物について情報を加える	competitor, which is · products, which makes · people who are
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is sponsored · have is called · is produced

注意したい発音

話し手はyou're, I'm, isn'tなど、短縮形や弱形を75回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: thorough /ˈθʌɹoʊ/, anthropic /ænˈθɹɒp.ɪk/
「sh」と「zh」の音: adoption /əˈdɑp.ʃən/, shout /ʃaʊt/, certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/, optional /ˈɑp.ʃə.nəl/
長い単語（アクセントの位置に注意）: certificate /səˈtɪf.ɪ.kɪt/, interactive /ˌɪn.tɚˈæk.tɪv/, ecosystem /ˈikoʊˌsɪstəm/, fundamental /ˌfʌn.dəˈmɛn.təl/, primarily /ˈpɹaɪ.mə.ɹə.li/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: regardless /ɹɪˈɡɑɹd.lɪs/, honorable /ˈɑn.ə.ɹəbl̩/, tutorial /ˌtjuːˈtɔːɹɪəl/, primarily /ˈpɹaɪ.mə.ɹə.li/, builder /ˈbɪl.dəɹ/
/v/ — /b/ にならないように、上の歯を下唇に当てる: interactive /ˌɪn.tɚˈæk.tɪv/, vendor /ˈvɛn.dɚ/, flavor /ˈfleɪvə/, intensive /ɪnˈtɛnsɪv/, vibe /vaɪb/
/f/ — 「フ」ではなく、上の歯と下唇で出す: certificate /səˈtɪf.ɪ.kɪt/, framework /ˈfɹeɪm.wɜːk/, flavor /ˈfleɪvə/, fundamental /ˌfʌn.dəˈmɛn.təl/, format /ˈfɔːɹ.mæt/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。certificate, skill, questなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Here's the AI Roadmap You Actually Need - 動画で英語スピーキングを学ぶ

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