この講座は、プラスαの学びとして、情報学と保育学という異なる学問領域の知識を統合し、新たな視点やアプローチを得ることができることを企図した大学生・社会人向けの講座です。また、保育学をベースとして、今後、保育学の知見をAIシステムの開発や応用に活かすことで、子どもたちの学びや成長をサポートする新しい方法を追求したいという方を念頭に置いて、講座を作成しています。この講座では、ICTを活用した保育環境や教育プログラムの設計方法や実践例について取り上げ、関連する知識とスキルの向上を支援します。
この講座は、プラスαの学びとして、情報学と保育学という異なる学問領域の知識を統合し、新たな視点やアプローチを得ることができることを企図した大学生・社会人向けの講座です。また、保育学をベースとして、今後、保育学の知見をAIシステムの開発や応用に活かすことで、子どもたちの学びや成長をサポートする新しい方法を追求したいという方を念頭に置いて、講座を作成しています。この講座では、ICTを活用した保育環境や教育プログラムの設計方法や実践例について取り上げ、関連する知識とスキルの向上を支援します。
同學們，你們是否常常因為寫作極為繁瑣而感到煩躁呢？是不是常常需要花費很長時間才能完成一篇論文呢？別擔心，我們有好消息要告訴你們！現在，你可以輕鬆地寫出一篇完美的論文，並且只需要花費 30 分鐘的時間！是的，你沒有聽錯，只需要短短的 30 分鐘，就能夠寫出一篇高質量的論文！
我們的「教你30分鐘用AI寫好論文」線上課程，將會教你如何使用 AI 技術，快速完成論文寫作。透過高效的論文指令，你用極短時間就學會如何運用 AI 技術，提高論文寫作效率。課程運用了多種 AI 寫作工具，包括論文編寫、問卷調查、論文參考、AI 加入註解、AI 降重、AI 潤色語言和 AI 創作簡報等，讓你可以輕鬆地完成論文寫作。
此外，我們的課程講師KEN，已教授不少學生運用 AI 完成論文的經驗，論文亦獲得優良成績。他將會親自教你如何使用 AI 技術，讓你的論文寫作變得更加容易。現在，你不需要再煩惱如何寫出一篇高質量的論文，也不需要花費很長時間才能完成論文寫作了。你只需要參加我們的「教你30分鐘用AI寫好論文」線上課程，就能夠輕鬆地寫出一篇完美的論文，並且提高你的論文寫作效率。
報名參加本課程，你不僅能夠學會如何運用 AI 技術，快速提高論文寫作效率，還能夠獲得實戰經驗，提高論文寫作能力。同時，你還能夠了解 AI 技術在論文寫作中的應用，讓你更加深入地了解 AI 技術。報名本課程，你需要具備基本的論文寫作知識，對 AI 技術有基本認識，確保電腦設備已經連接互聯網，並且已經安裝好 Google Chrome 瀏覽器。
所以，學生們，現在就報名參加我們的「教你30分鐘用AI寫好論文」線上課程，讓你的論文寫作變得更加容易！
課程介紹
本課程將介紹如何使用AI技術用30分鐘快速完成論文寫作，透過實作演練，學員可以學會如何運用AI技術，快速提高論文寫作效率。
課程運用粵語授課，已內箝繁體中文字幕，並有簡體中文字幕選擇。
第二章 AI寫作工具介紹
第三章 AI製作問卷及加入註解
第四章 AI降重及潤色
第五章 AI創作簡報
第六章 NOTION AI 的18種基本功能
預覽 AI論文工具推介
同學們，你們是否常常因為寫作極為繁瑣而感到煩躁呢？是不是常常需要花費很長時間才能完成一篇論文呢？別擔心，我們有好消息要告訴你們！現在，你可以輕鬆地寫出一篇完美的論文，並且只需要花費 30 分鐘的時間！是的，你沒有聽錯，只需要短短的 30 分鐘，就能夠寫出一篇高質量的論文！
我們的「教你30分鐘用AI寫好論文」線上課程，將會教你如何使用 AI 技術，快速完成論文寫作。透過高效的論文指令，你用極短時間就學會如何運用 AI 技術，提高論文寫作效率。課程運用了多種 AI 寫作工具，包括論文編寫、問卷調查、論文參考、AI 加入註解、AI 降重、AI 潤色語言和 AI 創作簡報等，讓你可以輕鬆地完成論文寫作。
此外，我們的課程講師KEN，已教授不少學生運用 AI 完成論文的經驗，論文亦獲得優良成績。他將會親自教你如何使用 AI 技術，讓你的論文寫作變得更加容易。現在，你不需要再煩惱如何寫出一篇高質量的論文，也不需要花費很長時間才能完成論文寫作了。你只需要參加我們的「教你30分鐘用AI寫好論文」線上課程，就能夠輕鬆地寫出一篇完美的論文，並且提高你的論文寫作效率。
報名參加本課程，你不僅能夠學會如何運用 AI 技術，快速提高論文寫作效率，還能夠獲得實戰經驗，提高論文寫作能力。同時，你還能夠了解 AI 技術在論文寫作中的應用，讓你更加深入地了解 AI 技術。報名本課程，你需要具備基本的論文寫作知識，對 AI 技術有基本認識，確保電腦設備已經連接互聯網，並且已經安裝好 Google Chrome 瀏覽器。
所以，學生們，現在就報名參加我們的「教你30分鐘用AI寫好論文」線上課程，讓你的論文寫作變得更加容易！
課程介紹
本課程將介紹如何使用AI技術用30分鐘快速完成論文寫作，透過實作演練，學員可以學會如何運用AI技術，快速提高論文寫作效率。
課程運用粵語授課，已內箝繁體中文字幕，並有簡體中文字幕選擇。
第二章 AI寫作工具介紹
第三章 AI製作問卷及加入註解
第四章 AI降重及潤色
第五章 AI創作簡報
第六章 NOTION AI 的18種基本功能
預覽 AI論文工具推介
The Future is Now: A Course on Integrating AI and ChatGPT in Clinical Practice
Thank you for your consideration to join our course on "Mastering AI and ChatGPT Applications in Healthcare"
We'll delve into the fascinating world of ChatGPT and its applications in healthcare. We'll explore how this advanced language model can be harnessed to enhance patient care, support clinical decision-making, and improve the overall healthcare experience. Throughout this presentation, we'll cover various chapters that provide a comprehensive understanding of ChatGPT, natural language processing (NLP), ethical considerations, model building, integrating clinical knowledge, and more.
By the end of this session, you'll have a solid foundation to leverage ChatGPT effectively in healthcare settings, while considering important ethical and privacy concerns.
Before we dive into the technical aspects, let's briefly discuss why ChatGPT holds immense potential for healthcare professionals. The healthcare landscape is evolving rapidly, with an increasing demand for personalized care, accessible information, and efficient decision support. ChatGPT can play a pivotal role in meeting these demands. With its natural language processing capabilities, ChatGPT can engage in human-like conversations, provide accurate medical information, assist in triage and symptom assessment, and offer mental health support. It complements the expertise of healthcare professionals, enhancing patient education, streamlining administrative tasks, and delivering timely, reliable, and empathetic care.
To provide you with a structured understanding of ChatGPT in healthcare, let's take a brief look at the course outline. We'll begin with an introduction to ChatGPT and its capabilities, followed by a discussion on the ethical considerations and limitations specific to healthcare applications. We'll then delve into the technical aspects, exploring NLP techniques, model building, and the integration of clinical knowledge and guidelines. Additionally, we'll address the crucial aspects of privacy, data security, and compliance with healthcare regulations when deploying chatbots in healthcare settings. Finally, we'll discuss user experience design, evaluation metrics, and ways to continually improve chatbot models while considering bias mitigation and fairness.
The Future is Now: A Course on Integrating AI and ChatGPT in Clinical Practice
Thank you for your consideration to join our course on "Mastering AI and ChatGPT Applications in Healthcare"
We'll delve into the fascinating world of ChatGPT and its applications in healthcare. We'll explore how this advanced language model can be harnessed to enhance patient care, support clinical decision-making, and improve the overall healthcare experience. Throughout this presentation, we'll cover various chapters that provide a comprehensive understanding of ChatGPT, natural language processing (NLP), ethical considerations, model building, integrating clinical knowledge, and more.
By the end of this session, you'll have a solid foundation to leverage ChatGPT effectively in healthcare settings, while considering important ethical and privacy concerns.
Before we dive into the technical aspects, let's briefly discuss why ChatGPT holds immense potential for healthcare professionals. The healthcare landscape is evolving rapidly, with an increasing demand for personalized care, accessible information, and efficient decision support. ChatGPT can play a pivotal role in meeting these demands. With its natural language processing capabilities, ChatGPT can engage in human-like conversations, provide accurate medical information, assist in triage and symptom assessment, and offer mental health support. It complements the expertise of healthcare professionals, enhancing patient education, streamlining administrative tasks, and delivering timely, reliable, and empathetic care.
To provide you with a structured understanding of ChatGPT in healthcare, let's take a brief look at the course outline. We'll begin with an introduction to ChatGPT and its capabilities, followed by a discussion on the ethical considerations and limitations specific to healthcare applications. We'll then delve into the technical aspects, exploring NLP techniques, model building, and the integration of clinical knowledge and guidelines. Additionally, we'll address the crucial aspects of privacy, data security, and compliance with healthcare regulations when deploying chatbots in healthcare settings. Finally, we'll discuss user experience design, evaluation metrics, and ways to continually improve chatbot models while considering bias mitigation and fairness.
Transform your PoCs & small projects into scalable AI Systems
You love to kickstart projects, but you always get stuck in the same development stage: a functional notebook - with a promising solution - that no one can access yet. The code is messy; refactoring & deploying the model seems daunting.
So you rummage online and crunch through Medium tutorials to learn about Machine Learning Engineering - but you haven't been able to glue all of the information together.
When it comes to making decisions between technologies and development paths, you get lost. You can't get other developers excited about your project.
Time to learn about MLE & MLOPS.
This training will aim to solve this by taking you through the design and engineering of an end-to-end Machine Learning project on top of the latest Cloud Platform technologies. It will cover a wide variety of concepts, structured in a way that allows you to understand the field step by step.
You'll get access to Lectures, Live Coding & Guided Labs to solve a practical use case that will serve as an example you can use for any of your future projects. By the end of the course, you should be more confident in your abilities to write efficient code at scale, deploy your models outside of your local environment, an design solutions iteratively.
Transform your PoCs & small projects into scalable AI Systems
You love to kickstart projects, but you always get stuck in the same development stage: a functional notebook - with a promising solution - that no one can access yet. The code is messy; refactoring & deploying the model seems daunting.
So you rummage online and crunch through Medium tutorials to learn about Machine Learning Engineering - but you haven't been able to glue all of the information together.
When it comes to making decisions between technologies and development paths, you get lost. You can't get other developers excited about your project.
Time to learn about MLE & MLOPS.
This training will aim to solve this by taking you through the design and engineering of an end-to-end Machine Learning project on top of the latest Cloud Platform technologies. It will cover a wide variety of concepts, structured in a way that allows you to understand the field step by step.
You'll get access to Lectures, Live Coding & Guided Labs to solve a practical use case that will serve as an example you can use for any of your future projects. By the end of the course, you should be more confident in your abilities to write efficient code at scale, deploy your models outside of your local environment, an design solutions iteratively.
I. Introduction
Definition of AI and biotechnology
Brief history of AI and biotechnology
Importance of AI-powered biotech in healthcare
Objectives of the book
II. Fundamentals of AI in Biotech
Overview of AI technologies used in biotech
Machine learning algorithms in biotech
Neural networks and deep learning in biotech
Natural language processing in biotech
Image recognition and computer vision in biotech
III. Applications of AI in Biotech
Drug discovery and development
Personalized medicine
Medical imaging and diagnosis
Disease monitoring and management
Precision agriculture and food security
IV. Challenges and Opportunities of AI in Biotech
Ethical and legal implications of AI in biotech
Data privacy and security concerns in AI-powered biotech
Lack of regulatory frameworks for AI in biotech
Integration of AI and human expertise in biotech
V. Case Studies on AI-Powered Biotech
Real-world examples of AI in biotech applications
Success stories of AI-powered biotech in healthcare
Challenges faced and solutions implemented in AI-powered biotech
VI. Future Perspectives on AI in Biotech
The potential impact of AI in biotech
The future of AI-powered biotech in healthcare
Technological advancements and their potential impact on AI in biotech
New trends and opportunities for AI-powered biotech in the future
VII. Conclusion
Summary of the key points discussed in the book
Future directions for research in AI-powered biotech
Final thoughts on the potential impact of AI in biotech in the future
I. Introduction
Definition of AI and biotechnology
Brief history of AI and biotechnology
Importance of AI-powered biotech in healthcare
Objectives of the book
II. Fundamentals of AI in Biotech
Overview of AI technologies used in biotech
Machine learning algorithms in biotech
Neural networks and deep learning in biotech
Natural language processing in biotech
Image recognition and computer vision in biotech
III. Applications of AI in Biotech
Drug discovery and development
Personalized medicine
Medical imaging and diagnosis
Disease monitoring and management
Precision agriculture and food security
IV. Challenges and Opportunities of AI in Biotech
Ethical and legal implications of AI in biotech
Data privacy and security concerns in AI-powered biotech
Lack of regulatory frameworks for AI in biotech
Integration of AI and human expertise in biotech
V. Case Studies on AI-Powered Biotech
Real-world examples of AI in biotech applications
Success stories of AI-powered biotech in healthcare
Challenges faced and solutions implemented in AI-powered biotech
VI. Future Perspectives on AI in Biotech
The potential impact of AI in biotech
The future of AI-powered biotech in healthcare
Technological advancements and their potential impact on AI in biotech
New trends and opportunities for AI-powered biotech in the future
VII. Conclusion
Summary of the key points discussed in the book
Future directions for research in AI-powered biotech
Final thoughts on the potential impact of AI in biotech in the future