Artificial Intelligence

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.


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AI Essentials

Complete Roadmap to learn Generative AI in 2 months 👇👇

Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.

Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.

Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.

Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn Generative AI 👇👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Deep Learning Nanodegree Program with Real-world Projects

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Important Data Science Libraries

Python Facial Recognition System Roadmap

Stage 1: Learn Python basics and OpenCV library.
Stage 2: Preprocess images with Gaussian blur.
Stage 3: Implement face detection using Haar cascades or DNNs.
Stage 4: Extract facial embeddings using libraries like dlib or FaceNet.
Stage 5: Train a classifier (SVM/KNN) for recognition.
Stage 6: Test on datasets.
Stage 7: Build a GUI with Tkinter or PyQt.
Stage 8: Deploy using Flask or FastAPI.

🏆 – Python Facial Recognition System.

New ai tools to explore this year

Here's a simple but powerful test to see the intelligence of an AI model. (The answer is the strawberry is still on the table)

Go ahead and ask any model this:

Assume the laws of physics on Earth. A small
strawberry is put into a normal cup and the cup is
placed upside down on a table. Someone then takes
the cup and puts it inside the microwave. Where is the
strawberry now? Explain your reasoning step by step.

💰 Building The Machine Learning Model

2025 Business Toolkit For Solopreneurs

1) ChatGPT - Generate content ideas
2) Notion - Second brain
3) Notion Calendar - Time management
4) Canva - Graphic design
5) Loom - Screen recording
6) Capcut - Video editing
7) Later - Social media management
8) Flodesk - Capture email leads
9) Calendly - Schedule calls
10) Gumroad - Sell digital products
11) DropboxSign - Contracts
12) Zapier - Automation
13) Stripe - Payment Processor
14) Google Drive - Storage
15) Opal - App blocking

Open Source LLMs

AI Concepts Explained

AI outcomes

If you need to share an ML model for web app development, create an API instead of saving it to a file. This avoids environment and security issues, allows access from various languages and platforms, and simplifies integration. Here's how to make an ML API with FastAPI.

Free AI Courses

Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?

In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.

Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.

Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.

Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.

Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.

I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.

📌 Introduction to Deep Learning