How to master ChatGPT-4o....
The secret? Prompt engineering.
These 9 frameworks will help you!
APE
↳ Action, Purpose, Expectation
Action: Define the job or activity.
Purpose: Discuss the goal.
Expectation: State the desired outcome.
RACE
↳ Role, Action, Context, Expectation
Role: Specify ChatGPT's role.
Action: Detail the necessary action.
Context: Provide situational details.
Expectation: Describe the expected outcome.
COAST
↳ Context, Objective, Actions, Scenario, Task
Context: Set the stage.
Objective: Describe the goal.
Actions: Explain needed steps.
Scenario: Describe the situation.
Task: Outline the task.
TAG
↳ Task, Action, Goal
Task: Define the task.
Action: Describe the steps.
Goal: Explain the end goal.
RISE
↳ Role, Input, Steps, Expectation
Role: Specify ChatGPT's role.
Input: Provide necessary information.
Steps: Detail the steps.
Expectation: Describe the result.
TRACE
↳ Task, Request, Action, Context, Example
Task: Define the task.
Request: Describe the need.
Action: State the required action.
Context: Provide the situation.
Example: Illustrate with an example.
ERA
↳ Expectation, Role, Action
Expectation: Describe the desired result.
Role: Specify ChatGPT's role.
Action: Specify needed actions.
CARE
↳ Context, Action, Result, Example
Context: Set the stage.
Action: Describe the task.
Result: Describe the outcome.
Example: Give an illustration.
ROSES
↳ Role, Objective, Scenario, Expected Solution, Steps
Role: Specify ChatGPT's role.
Objective: State the goal or aim.
Scenario: Describe the situation.
Expected Solution: Define the outcome.
Steps: Ask for necessary actions to reach solution.
Join for more: https://t.me/machinelearning_deeplearning
The secret? Prompt engineering.
These 9 frameworks will help you!
APE
↳ Action, Purpose, Expectation
Action: Define the job or activity.
Purpose: Discuss the goal.
Expectation: State the desired outcome.
RACE
↳ Role, Action, Context, Expectation
Role: Specify ChatGPT's role.
Action: Detail the necessary action.
Context: Provide situational details.
Expectation: Describe the expected outcome.
COAST
↳ Context, Objective, Actions, Scenario, Task
Context: Set the stage.
Objective: Describe the goal.
Actions: Explain needed steps.
Scenario: Describe the situation.
Task: Outline the task.
TAG
↳ Task, Action, Goal
Task: Define the task.
Action: Describe the steps.
Goal: Explain the end goal.
RISE
↳ Role, Input, Steps, Expectation
Role: Specify ChatGPT's role.
Input: Provide necessary information.
Steps: Detail the steps.
Expectation: Describe the result.
TRACE
↳ Task, Request, Action, Context, Example
Task: Define the task.
Request: Describe the need.
Action: State the required action.
Context: Provide the situation.
Example: Illustrate with an example.
ERA
↳ Expectation, Role, Action
Expectation: Describe the desired result.
Role: Specify ChatGPT's role.
Action: Specify needed actions.
CARE
↳ Context, Action, Result, Example
Context: Set the stage.
Action: Describe the task.
Result: Describe the outcome.
Example: Give an illustration.
ROSES
↳ Role, Objective, Scenario, Expected Solution, Steps
Role: Specify ChatGPT's role.
Objective: State the goal or aim.
Scenario: Describe the situation.
Expected Solution: Define the outcome.
Steps: Ask for necessary actions to reach solution.
Join for more: https://t.me/machinelearning_deeplearning
👍13
ChatGPT Prompt to learn any skill
👇👇
(Tap on above text to copy)
👇👇
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)
👍13❤2🔥2
Want to use ChatGPT at lightning speed?
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
👍1
AI/ML (Daily Schedule) 👨🏻💻
Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study
Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest
Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing
Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest
Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study
Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest
Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing
Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest
Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍13❤5😁2🙈2
Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
👍18❤4🔥3
📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
👍12
🏆 – AI/ML Engineer
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
👍19❤8
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
Like for more ❤️
All the best 👍👍
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
Like for more ❤️
All the best 👍👍
👍9❤5🥱1