ChatGPT Prompt to learn any skill
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(Tap on above text to copy)
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I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information(Tap on above text to copy)
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Breaking into ML Engineering can be very confusing in 2024!
Should I learn TensorFlow or PyTorch? Python or R? Scikit-learn or XGBoost? GCP or AWS? FastAPI or Streamlit?
Fundamental principles are more important than tools:
- understanding statistics and deep learning is more important than TensorFlow vs PyTorch.
- understanding functional and object-oriented programming is more important than Python or R.
- understanding feature engineering is more important than Scikit-learn vs XGBoost.
- understanding scalable and resilient architectures is more important than GCP or AWS.
- understanding models serving is more important than FastAPI or Streamlit.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Join for more: https://t.me/machinelearning_deeplearning
All the best 👍👍
Should I learn TensorFlow or PyTorch? Python or R? Scikit-learn or XGBoost? GCP or AWS? FastAPI or Streamlit?
Fundamental principles are more important than tools:
- understanding statistics and deep learning is more important than TensorFlow vs PyTorch.
- understanding functional and object-oriented programming is more important than Python or R.
- understanding feature engineering is more important than Scikit-learn vs XGBoost.
- understanding scalable and resilient architectures is more important than GCP or AWS.
- understanding models serving is more important than FastAPI or Streamlit.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Join for more: https://t.me/machinelearning_deeplearning
All the best 👍👍
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Unpopular opinion:
ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.
ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.
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Some useful AI tools in 2024
Solves anything -> Gemini
Text to image -> Adobe Firefly
Create AI Avatar -> HeyGen
Create Art -> Midjourney
Video editing -> Topview AI
Text to video -> Pika 1.0
Create logo -> logodiffusion
Create interface -> Uiverse
Creates copycats -> Tome
Essay assistant -> Jenni AI
Repetitive tasks -> Zapier
Copies your voice -> Eleven Labs
Rewrite anything -> Quillbot
Drawing assistant -> Autodraw
Create slide deck -> Autodraw
Write any emails -> Addy AI
Summarize notes -> Wordtune
Create music -> Soundraw
Solves anything -> Gemini
Text to image -> Adobe Firefly
Create AI Avatar -> HeyGen
Create Art -> Midjourney
Video editing -> Topview AI
Text to video -> Pika 1.0
Create logo -> logodiffusion
Create interface -> Uiverse
Creates copycats -> Tome
Essay assistant -> Jenni AI
Repetitive tasks -> Zapier
Copies your voice -> Eleven Labs
Rewrite anything -> Quillbot
Drawing assistant -> Autodraw
Create slide deck -> Autodraw
Write any emails -> Addy AI
Summarize notes -> Wordtune
Create music -> Soundraw
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Artificial intelligence (AI) and machine learning (ML) are changing the future of work. While both terms seem similar, machine learning is actually a specific technique used by AI designers to achieve artificially intelligent computer programs. Knowing the basics of how AI and ML relate to each other can help you navigate these technologies as they transform the work landscape, enabling you to effectively contribute to AI-driven projects or lead your own AI initiatives.
In this reading, you'll explore some of the ML techniques AI designers use to build AI programs, deepening your understanding of how ML leverages data to make decisions and perform tasks. You'll also explore how ML techniques have paved the way for generative AI.
AI development techniques
Artificial intelligence refers to computer programs that can complete cognitive tasks typically associated with human intelligence. There are two main techniques used to design AI programs:
Rule-based techniques involve creating AI programs that strictly follow predefined rules to make decisions. For example, a spam filter using rule-based techniques might block emails that contain specific keywords using its predefined logic.
Machine learning techniques involve creating AI programs that can analyze and learn from patterns in data to make independent decisions. For example, a spam filter using these techniques might flag potential spam for the recipient to review, preventing automatic blocking. If the recipient marks emails from trusted sources as safe, the spam filter learns and adapts its logic to include similar emails from that sender in the future.
AI tools can use either rule-based or ML techniques, or even a combination of both. In general, rule-based techniques are commonly used for tasks that require rigidity, such as blocking messages from untrusted senders that are obviously spam, like requests for bank transfers or private information. Conversely, ML techniques are better suited for tasks demanding flexibility and adaptability, like learning to recognize that messages from trusted senders containing typos are not spam.
Approaches to training ML programs
Recall that machine learning is a subset of AI focused on developing computer programs that can analyze data to make decisions or predictions. AI designers often use ML in their AI programs because it doesn’t have the limitations of rule-based techniques.
A large circle representing AI with a smaller circle representing ML inside.
There are three common approaches to training ML programs:
Supervised learning
Unsupervised learning
Reinforcement learning
Supervised learning
In this approach, the ML program learns from a labeled training set. A labeled training set includes data that is labeled or tagged, which provides context and meaning to the data. For instance, an email spam filter that's trained with supervised learning would use a training set of emails that are labeled as “spam” or “not spam.” Supervised learning is often used when there's a specific output in mind.
Unsupervised learning
In this approach, the ML program learns from an unlabeled training set. An unlabeled training set includes data that does not have labels or tags. For instance, ML might be used to analyze a dataset of unsorted email messages and find patterns in topics, keywords, or contacts. In other words, unsupervised learning is used to identify patterns in data without a specific output in mind.
Reinforcement learning
In this approach, the ML program uses trial-and-error to learn which actions lead to the best outcome. The program learns to do this by getting rewarded for making good choices that lead to the desired results. Reinforcement learning is commonly used by conversational AI tools. As these tools receive feedback from users and AI designers, they learn to generate effective responses.
In this reading, you'll explore some of the ML techniques AI designers use to build AI programs, deepening your understanding of how ML leverages data to make decisions and perform tasks. You'll also explore how ML techniques have paved the way for generative AI.
AI development techniques
Artificial intelligence refers to computer programs that can complete cognitive tasks typically associated with human intelligence. There are two main techniques used to design AI programs:
Rule-based techniques involve creating AI programs that strictly follow predefined rules to make decisions. For example, a spam filter using rule-based techniques might block emails that contain specific keywords using its predefined logic.
Machine learning techniques involve creating AI programs that can analyze and learn from patterns in data to make independent decisions. For example, a spam filter using these techniques might flag potential spam for the recipient to review, preventing automatic blocking. If the recipient marks emails from trusted sources as safe, the spam filter learns and adapts its logic to include similar emails from that sender in the future.
AI tools can use either rule-based or ML techniques, or even a combination of both. In general, rule-based techniques are commonly used for tasks that require rigidity, such as blocking messages from untrusted senders that are obviously spam, like requests for bank transfers or private information. Conversely, ML techniques are better suited for tasks demanding flexibility and adaptability, like learning to recognize that messages from trusted senders containing typos are not spam.
Approaches to training ML programs
Recall that machine learning is a subset of AI focused on developing computer programs that can analyze data to make decisions or predictions. AI designers often use ML in their AI programs because it doesn’t have the limitations of rule-based techniques.
A large circle representing AI with a smaller circle representing ML inside.
There are three common approaches to training ML programs:
Supervised learning
Unsupervised learning
Reinforcement learning
Supervised learning
In this approach, the ML program learns from a labeled training set. A labeled training set includes data that is labeled or tagged, which provides context and meaning to the data. For instance, an email spam filter that's trained with supervised learning would use a training set of emails that are labeled as “spam” or “not spam.” Supervised learning is often used when there's a specific output in mind.
Unsupervised learning
In this approach, the ML program learns from an unlabeled training set. An unlabeled training set includes data that does not have labels or tags. For instance, ML might be used to analyze a dataset of unsorted email messages and find patterns in topics, keywords, or contacts. In other words, unsupervised learning is used to identify patterns in data without a specific output in mind.
Reinforcement learning
In this approach, the ML program uses trial-and-error to learn which actions lead to the best outcome. The program learns to do this by getting rewarded for making good choices that lead to the desired results. Reinforcement learning is commonly used by conversational AI tools. As these tools receive feedback from users and AI designers, they learn to generate effective responses.
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