シャドーイング練習: A Practical Guide To Becoming An AI Engineer (2026) - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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AI engineers are some of the highest paid roles in tech right now.
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Median pay is around $180,000 a year according to built-in, and at companies like OpenAI or Meta.
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Senior engineers can make between $860,000 to over $1.27 million.
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The problem is most people try to break into AI by learning the wrong skills in the wrong order, wasting months on things companies don't even hire for.
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By the end of this video, you'll know what AI engineers actually do, what skills matter, if you need math or ML,
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which projects impress recruiters, and the fastest path to getting hired in 2026.
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All in under 5 minutes.
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But before we start, I would really appreciate it if you could hit that like button and subscribe.
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Alright, let's start.
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First, what do AI engineers actually do?
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People picture someone with a PhD writing neural networks from scratch or doing complex research.
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But that's not the role companies are hiring for.
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AI engineers build applications and systems that use existing models to solve real problems.
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Think of it like this.
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A researcher invents a new engine, but an AI engineer takes that engine and builds the car people can drive.
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Companies need people who can make models work in production, like chatbots, assistants, or automation pipelines.
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There are over 500,000 open AI and ML roles globally, and most want engineers who can integrate existing models, not invent new ones.
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The job market is huge and growing.
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AI-related roles grew 25% in the first quarter of 2025 alone, and nearly 40% of the in-demand skills aren't widely held in the current workforce.
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That's an enormous opportunity if you start learning now.
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I've broken the roadmap into four phases.
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Phase one is fundamentals.
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You need Python, not tutorial style, but production-ready code.
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Handle APIs, JSON, files, and errors confidently.
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Git and GitHub aren't optional, your repos can act as a portfolio.
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You also need basic ML concepts, what a model is, training versus inference, embeddings, and core terminology.
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This phase takes one and a half to three months, but mastering it saves months of frustration later.
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Phase two is large language model integration.
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Prompt engineering is essential.
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And no, I am not talking about just typing questions.
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I am talking about consistent, reliable results.
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Learn system prompts, few-shot learning, chain of thought reasoning, and output formatting.
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Next, work with APIs.
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OpenAI's API is the most common, but you should also explore Anthropix API and Hugging Face's open source models.
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Know how to send requests, handle responses, manage tokens, and control costs.
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By the end of this phase, which takes two to three months on average, you should be able to build a simple AI application that takes user input, sends it to a model, and returns structured output.
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Phase three is about real production systems.
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Start with Langchain to connect models, tools, memory, and multi-step logic into pipelines.
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Learn RAG, Retrieval Augmented Generation, to give models access to your documents, databases, and internal knowledge for accurate answers.
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You should also understand that AI agents go beyond chat, they perform actions, call APIs, update records, and trigger workflows.
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And this is where MCP comes in.
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The model context protocol ensures AI interacts safely with external systems like GitHub, Zapier, and Google Docs.
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Finally, understand LLM Ops.
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Prompt versioning, monitoring, cost management, and handling updates.
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Phase three usually takes two to three months and gives you the skills to build production-ready systems.
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Finally, we have phase four, which is about turning skills into a job.
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Build portfolio projects that showcase different capabilities.
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Create a RAG-powered decision support system with embeddings, semantic search, structured outputs, and confidence scores.
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Or make an AI workflow orchestrator that ingests tickets, emails, or logs, classifies and prioritizes them, applies business rules, and triggers actions.
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Don't forget to keep your code clean, document everything, and make demo videos.
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Optional certifications like the ones from Azure or Databricks can help, but they're not required.
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On your resume, clearly list Python, Git, Langchain, RAG, vector databases, and APIs, and link to your GitHub portfolio.
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It's true that the AI engineering field moves fast, with new models, frameworks, and techniques appearing constantly.
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But the good news is that fundamentals like Python, prompt engineering, RAG, and agents don't change.
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Start now, build actively, make mistakes, learn, and iterate.
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That's how you'll become a competitive AI engineer in 2026.
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Pick one skill from this video and start today.
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Don't wait until you feel ready.
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And if you are looking for a place to start your journey, then you should check out Scrimba, today's sponsor.
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Scrimba flipped the whole tutorial format on its head by turning the video itself into the IDE.
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These are called scrims, interactive lessons where you can pause the video, edit the code directly inside it, break things safely, and actually understand why stuff works instead of blindly copying it.
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No more alt-tab gymnastics between YouTube and your editor.
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Scrimba not only has dozens of tutorials and lessons, they have full career paths for front-end, and full stack devs, there is even an AI engineer path.
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With real projects, structured progression, and practical advice you won't find in typical courses, like how code reviews actually work,
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how teams collaborate, and how to not sound like an NPC in interviews.
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So what are you waiting for?
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Go touch some scrims using my link in the description and get 20% off their pro plans, which unlocks everything.
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This was CodeHead with yet another TechRant.
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If you enjoyed it, please leave a like and subscribe.
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Lights out!
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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C1レベルのスピーキングレッスンは、動画「A Practical Guide To Becoming An AI Engineer」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：model, engineer, phase, Api, skill。 この動画には、シャドーイング用の文が70文、単語が930語あります。 音声の長さは5:05です。 話す速さは速く、1分あたり約183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。 英語の頻出3,000語に含まれる単語は75%だけなので、語彙は難しめです。
この動画の重要語彙
動画の中で特に難しい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| tutorial 名詞 | /ˌtjuːˈtɔːɹɪəl/ | 学習用, チュートリアル |
| workflow 名詞 | /ˈwɝkfloʊ/ | ワークフロー |
| invent 動詞 | /ɪnˈvɛnt/ | 発明する |
| subscribe 動詞 | /səbˈskɹaɪb/ | 購読する |
| pipeline 名詞 | /ˈpaɪpˌlaɪn/ | パイプライン |
| chatbot 名詞 | /ˈtʃætbɑt/ | チャットボット |
| classify 動詞 | /ˈklæs.əˌfaɪ/ | 分類する |
| prioritize 動詞 | /pɹaɪˈɒɹ.ə.taɪz/ | 優先させる |
| semantic 形容詞 | /sɪˈmæntɪk/ | 意味的な |
| collaborate 動詞 | /kəˈlæb.ə.ɹeɪt/ | 協力する, 共同して働く |
| gymnastics 名詞 | /d͡ʒɪmˈnæs.tɪks/ | 体操, 体育 |
| terminology 名詞 | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | 用語, 専門語 |
| automation 名詞 | /ˌɔ.təˈmeɪ.ʃən/ | 自動化, オートメーション |
| token 名詞 | /ˈtoʊkən/ | トークン |
| unlock 動詞 | /ʌnˈlɑk/ | 開ける, 解錠する |
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 |
| get off 動詞 | 降りる |
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。
- That's an enormous opportunity if you start learning now.
- I've broken the roadmap into four phases.
- Don't wait until you feel ready.
注意したい発音
話し手はdon't, aren't, you'llなど、短縮形や弱形を11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。
- 「sh」と「zh」の音: optional /ˈɑp.ʃə.nəl/, automation /ˌɔ.təˈmeɪ.ʃən/, progression /pɹəˈɡɹɛʃən/, showcase /ˈʃoʊˌkeɪs/, frustration /fɹʌsˈtɹeɪ.ʃən/
- 長い単語（アクセントの位置に注意）: confidently /ˈkɑnfɪdəntli/, prioritize /pɹaɪˈɒɹ.ə.taɪz/, collaborate /kəˈlæb.ə.ɹeɪt/, terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automation /ˌɔ.təˈmeɪ.ʃən/
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: tutorial /ˌtjuːˈtɔːɹɪəl/, collaborate /kəˈlæb.ə.ɹeɪt/, neural /ˈnʊɹəl/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: invent /ɪnˈvɛnt/, vector /ˈvɛktɚ/, interactive /ˌɪn.tɚˈæk.tɪv/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: workflow /ˈwɝkfloʊ/, classify /ˈklæs.əˌfaɪ/, confidently /ˈkɑnfɪdəntli/, inference /ˈɪn.fə.ɹəns/, workforce /ˈwɝk.foɹs/
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。tutorial, workflow, invent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。