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Course Description: This course will introduce students to the exciting field of Generative Artificial Intelligence (AI) and its applications in engineering. Through a combination of lectures, hands-on projects, and discussions, students will gain a comprehensive understanding of the principles and techniques used in generative AI. The course will cover topics such as machine learning, deep learning, natural language processing, and computer vision, all within the context of generative AI. Students will learn how to use popular tools and frameworks such as TensorFlow, PyTorch, and GPT-3 to build and train generative AI models. Throughout the course, students will work on real-world engineering problems and develop their own generative AI solutions. They will also have the opportunity to collaborate with their peers and receive feedback from experienced instructors. By the end of this course, students will have a strong foundation in generative AI and be able to apply their knowledge to various engineering fields, including robotics, automation, and data analysis. Join us and discover the endless possibilities of generative AI in engineering!

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Has discount
Expiry period Lifetime
Made in English
Last updated at Mon May 2024
Level
Beginner
Total lectures 15
Total quizzes 0
Total duration 174:09:20 Hours
Total enrolment 0
Number of reviews 0
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Short description Course Description: This course will introduce students to the exciting field of Generative Artificial Intelligence (AI) and its applications in engineering. Through a combination of lectures, hands-on projects, and discussions, students will gain a comprehensive understanding of the principles and techniques used in generative AI. The course will cover topics such as machine learning, deep learning, natural language processing, and computer vision, all within the context of generative AI. Students will learn how to use popular tools and frameworks such as TensorFlow, PyTorch, and GPT-3 to build and train generative AI models. Throughout the course, students will work on real-world engineering problems and develop their own generative AI solutions. They will also have the opportunity to collaborate with their peers and receive feedback from experienced instructors. By the end of this course, students will have a strong foundation in generative AI and be able to apply their knowledge to various engineering fields, including robotics, automation, and data analysis. Join us and discover the endless possibilities of generative AI in engineering!
Outcomes
  • 1. Natural Language Processing (NLP): NLP is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. This is essential for chatbots and other conversational AI systems.
  • 2. Machine Learning: Machine learning is a subset of AI that involves training algorithms to make predictions or decisions based on data. This is crucial for chatbots to learn and improve their responses over time.
  • 3. Conversational Design: Conversational design is the process of creating human-like conversations between humans and machines. This involves understanding user needs, designing conversation flows, and creating a personality for the chatbot.
  • 4. Sentiment Analysis: Sentiment analysis is the process of identifying and understanding the emotions and opinions expressed in text. This is important for chatbots to be able to respond appropriately and empathetically to users.
  • 5. Natural Language Generation (NLG): NLG is the process of generating human-like text from data. This is useful for chatbots to create personalized and relevant responses to user queries.
Requirements
  • 1. Strong foundation in programming languages: ChatGPT prompts engineering requires a strong understanding of programming languages such as Python, Java, or C++. This is essential for building and customizing the chatbot's functionality and integrating it with other systems.
  • 2. Natural Language Processing (NLP) skills: NLP is a key component of chatGPT prompts engineering as it enables the chatbot to understand and respond to human language. Knowledge of NLP techniques and tools such as NLTK, spaCy, or Gensim is crucial for developing effective chatbot prompts.
  • 3. Familiarity with machine learning algorithms: ChatGPT prompts engineering involves training the chatbot on large datasets to improve its responses. Knowledge of machine learning algorithms such as deep learning, reinforcement learning, and natural language generation is necessary for this task.
  • 4. Understanding of chatbot platforms: ChatGPT prompts engineering often involves working with chatbot platforms such as Dialogflow, IBM Watson, or Microsoft Bot Framework. Familiarity with these platforms and their features is important for building and deploying chatbots.
  • 5. Creativity and problem-solving skills: As chatGPT prompts engineering is a relatively new field, there may not always be a clear solution to a problem. Engineers must be creative and have strong problem-solving skills to come up with innovative solutions and improve the chatbot's performance.