AIってなんだ。イメージで理解しておきたい人のための超入門講座 from Udemy

「AIってなんだ」

このコースを通じて、数式や難しい概念を用いることなく、画像や図からイメージでAIを理解し、この疑問に答えることができるようになります。

また、AIについてこれから学習を始めたい方にも適した内容です。全体像を俯瞰してから具体的な学習に取り組むことで、効率的に学習することができるはずです。

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>このコースの特徴

数式や難しい概念を使わずに、図や写真から視覚的に理解できる。
端的なコースで１日で完結する。
単語の説明にとどまらず、背景を知ることができる。

.コース修了時にイメージで理解できている単語

機械学習・ディープラーニング・学習と推論・強いAI・弱いAI・フレーム問題・シンボルグラウンディング問題・チューリングテスト・ニューラルネットワーク・パーセプトロン・教師あり/なし学習…

>このコースの受講をおすすめする人

「AIってなんだ」

このコースを通じて、数式や難しい概念を用いることなく、画像や図からイメージでAIを理解し、この疑問に答えることができるようになります。

ーーーーーーーーーーーーーーー

>このコースの特徴

数式や難しい概念を使わずに、図や写真から視覚的に理解できる。
端的なコースで１日で完結する。
単語の説明にとどまらず、背景を知ることができる。

.コース修了時にイメージで理解できている単語

>このコースの受講をおすすめする人

AIに何となく興味があるすべての人
学習を始めたいが、何から手をつけていいのかわからない人
機械学習やディープラーニングを中心に全体像を俯瞰したい人

>逆に、このコースをおすすめできない人

機械学習やディープラーニング等についてその理論を数学的に(厳密に)理解したい人
Pythonなどの言語を用いて実際にAIを構築したい人

全く知識がない状態から始め、数時間でAIのイメージを掴むことができます。さぁ、思い立った今から始めましょう！

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>チームや組織におすすめする講座を探している方へ

このコースは、AIに対する過度な期待や、早まった批判などをなくすことを目的として構成されています。受講を通じて、AIという技術に対するイメージを揃えることができます。誰しもがAIを活用できるようになった今、その理解をチーム・組織内である程度揃えておくことは重要です。コースの最後には理解度を確認できる演習テストを含めています。ぜひご活用ください。

>アップデート情報

2024.2.20 - コース全体の理解度を確認できる確認テストを追加しました

What's inside

Syllabus

機械学習とディープラーニングの概要とその限界についてイメージを掴むことができます。

AIの意味するところと、機械学習・ディープラーニングとはなにかを理解できます。

機械学習の学習と推論という過程を理解できます。

機械学習に適したタスクとは何かを考えます。

リンゴとバナナの分類タスクを例に、機械学習の学習とは何をすることなのかを考えます。

教師あり学習となし学習について理解できます。

機械学習に使う適切なデータについて考えます。

特徴量の選択について、その難しさを考えます。

ディープラーニングという技術についてその仕組みをイメージできるようになります。

機械と人間を判別することの難しさを考えます。

フレーム問題とシンボルグラウンディング問題について理解できます。

AIが人間を超えることはできるかについて考えます。

コースの理解度を確認できます。

ここまでに学習した内容を振り返ります。覚えておいてほしい重要な内容を確認するためのテストです。

ここから先へ

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suits beginners who want a basic understanding of AI concepts

Excellent visual aids to help learners develop an image of AI

Provides a foundation for learners who want to delve deeper into AI

Appropriate for those beginning their AI journey

Suitable for learners seeking a surface-level understanding of AI

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AIってなんだ。イメージで理解しておきたい人のための超入門講座 with these activities:

Brush up on Python

Show steps

Python is a programming language commonly used in AI. Review the basics or enhance your proficiency as needed

Browse courses on Python

Show steps

Complete an online refresher course for Python
Practice writing Python code and solving problems

Review linear algebra

Show steps

Linear algebra is an important mathematical foundation for AI. Review the key concepts to strengthen your understanding

Browse courses on Linear Algebra

Show steps

Go through your linear algebra notes or textbooks
Practice solving linear algebra problems

Learn about AI algorithms

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The algorithms of AI are at the very core of the technology. Familiarize yourself with them as best as you can

Browse courses on AI Algorithms

Show steps

Go online and choose a guided tutorial on AI algorithms
Learn a few different algorithm types and understand their strengths and limitations

One other activity

Expand to see all activities and additional details

Show all four activities

Practice applying AI concepts

Show steps

To truly grasp the capabilities of AI, work through targeted exercises and problems

Browse courses on AI Concepts

Show steps

Find a set of practice problems related to AI
Attempt to solve a variety of problems
Check your solutions and learn from your mistakes

Career center

Learners who complete AIってなんだ。イメージで理解しておきたい人のための超入門講座 will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to extract meaningful insights. This course can help build a foundation in machine learning and deep learning, which are essential skills for Data Scientists. The course also covers topics such as data preparation and feature engineering, which are critical for successful data analysis.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course can help build a foundation in machine learning and deep learning, which are essential skills for Machine Learning Engineers. The course also covers topics such as model evaluation and tuning, which are critical for successful machine learning model development.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course can help build a foundation in machine learning and deep learning, which are essential skills for Artificial Intelligence Engineers. The course also covers topics such as natural language processing and computer vision, which are critical for successful AI system development.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to extract meaningful insights. This course can help build a foundation in machine learning and deep learning, which are becoming increasingly important skills for Data Analysts. The course also covers topics such as data visualization and statistical analysis, which are critical for successful data analysis.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help build a foundation in machine learning and deep learning, which are becoming increasingly important skills for Software Engineers. The course also covers topics such as software design and development, which are critical for successful software engineering.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help build a foundation in machine learning and deep learning, which are becoming increasingly important skills for Product Managers. The course also covers topics such as product development and marketing, which are critical for successful product management.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts analyze business processes and make recommendations for improvement. This course can help build a foundation in machine learning and deep learning, which can be used to improve business processes. The course also covers topics such as data analysis and modeling, which are critical for successful business analysis.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze data and make investment decisions. This course can help build a foundation in machine learning and deep learning, which are becoming increasingly important skills for Quantitative Analysts. The course also covers topics such as financial modeling and risk management, which are critical for successful quantitative analysis.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers collect and analyze data about consumers and markets. This course can help build a foundation in machine learning and deep learning, which can be used to analyze market data. The course also covers topics such as survey design and data analysis, which are critical for successful marketing research.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can help build a foundation in machine learning and deep learning, which can be used to assess risk and uncertainty in various domains. The course also covers topics such as probability and statistics, which are critical for successful actuarial work.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve business operations. This course can help build a foundation in machine learning and deep learning, which can be used to improve business operations. The course also covers topics such as optimization and simulation, which are critical for successful operations research.

See salaries and explore the career path for Operations Research Analyst

Biostatistician

Biostatisticians use statistical methods to analyze biological data. This course can help build a foundation in machine learning and deep learning, which can be used to analyze biological data. The course also covers topics such as bioinformatics and statistical modeling, which are critical for successful biostatistics.

See salaries and explore the career path for Biostatistician

Financial Analyst

Financial Analysts analyze financial data and make investment recommendations. This course can help build a foundation in machine learning and deep learning, which are becoming increasingly important skills for Financial Analysts. The course also covers topics such as financial modeling and valuation, which are critical for successful financial analysis.

See salaries and explore the career path for Financial Analyst

Insurance Analyst

Insurance Analysts analyze insurance data and make recommendations for pricing and underwriting. This course can help build a foundation in machine learning and deep learning, which can be used to analyze insurance data. The course also covers topics such as actuarial science and risk management, which are critical for successful insurance analysis.

See salaries and explore the career path for Insurance Analyst

Risk Manager

Risk Managers identify, assess, and manage risks. This course can help build a foundation in machine learning and deep learning, which can be used to identify, assess, and manage risks. The course also covers topics such as risk assessment and mitigation, which are critical for successful risk management.

See salaries and explore the career path for Risk Manager