💰 Building The Machine Learning Model

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.

New ai tools to explore this year

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.

Important Data Science Libraries

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.

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Learn Python for Free

Prompt Engineering Course

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Google Cloud Generative AI Path

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AI Essentials

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

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

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Top 9 machine learning algorithms

Ai tools used by hackers

Master AI (Artificial Intelligence) in 10 days 👇👇

#AI

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Free Resources: https://t.me/machinelearning_deeplearning

Share for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

Machine Learning Algorithms for Classification Problems

Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence

1. Programming Languages:

Python

R

Java

Julia


2. AI Frameworks:

TensorFlow

PyTorch

Keras

MXNet

Caffe


3. Machine Learning Libraries:

Scikit-learn: For classical machine learning models.

XGBoost: For boosting algorithms.

LightGBM: For gradient boosting models.


4. Deep Learning Tools:

TensorFlow

PyTorch

Keras

Theano


5. Natural Language Processing (NLP) Tools:

NLTK (Natural Language Toolkit)

SpaCy

Hugging Face Transformers

Gensim


6. Computer Vision Libraries:

OpenCV

DLIB

Detectron2


7. Reinforcement Learning Frameworks:

Stable-Baselines3

RLlib

OpenAI Gym


8. AI Development Platforms:

IBM Watson

Google AI Platform

Microsoft AI


9. Data Visualization Tools:

Matplotlib

Seaborn

Plotly

Tableau


10. Robotics Frameworks:

ROS (Robot Operating System)

MoveIt!


11. Big Data Tools for AI:

Apache Spark

Hadoop


12. Cloud Platforms for AI Deployment:

Google Cloud AI

AWS SageMaker

Microsoft Azure AI


13. Popular AI APIs and Services:

Google Cloud Vision API

Microsoft Azure Cognitive Services

IBM Watson AI APIs


14. Learning Resources and Communities:

Kaggle

GitHub AI Projects

Papers with Code


Share with credits: https://t.me/machinelearning_deeplearning

ENJOY LEARNING 👍👍

10 Prompts to Transform You Into a Superhuman

1.Design the Ultimate Daily Schedule
Prompt: "Help me create the ultimate daily schedule that optimizes productivity and energy. Consider my waking hours from [specific start time] to [specific end time], including work tasks, breaks, meals, exercise, and personal development. Ensure the schedule is realistic, sustainable, and maximizes focus and efficiency."

2.Master Time-Blocking
Prompt: "Teach me how to implement time-blocking effectively in my daily routine. Show me how to prioritize my tasks into focused blocks, including specific examples for [type of tasks], and how to handle interruptions without losing momentum."

3.Eliminate Procrastination
Prompt: "Guide me through the process of eliminating procrastination. Include strategies for identifying my procrastination triggers, using tools like the Pomodoro technique, and creating a mindset that prioritizes action over delay for [specific tasks or goals]."

4.Build the Perfect Morning Routine
Prompt: "Help me craft a morning routine that sets the tone for a super-productive day. Include steps for waking up early, incorporating activities like exercise, journaling, and planning the day, and maintaining high energy levels throughout the morning."

5.Set and Achieve Goals
Prompt: "Guide me in setting SMART goals for [specific area] and breaking them into actionable steps. Include advice on tracking progress, staying motivated, and overcoming obstacles to ensure consistent progress and long-term success."

6.Master Deep Work
Prompt: "Show me how to integrate deep work sessions into my daily routine. Include strategies for minimizing distractions, creating an optimal workspace, and focusing intensely on high-priority tasks in [specific area of work]."

7.Develop Keystone Habits
Prompt: "Teach me how to identify and build keystone habits that will transform my productivity. Provide examples of habits in [specific area] that have a domino effect, such as regular exercise, daily planning, or consistent learning."

8.Automate Repetitive Tasks
Prompt: "Guide me in identifying and automating repetitive tasks in my personal and professional life. Include tools and systems for [specific tasks] that save time and allow me to focus on high-impact activities."

9.Master Priority Management
Prompt: "Show me how to prioritize tasks using methods like the Eisenhower Matrix or the 80/20 rule. Help me identify my most impactful tasks in [specific field] and create a system for focusing on what truly matters."

10.Implement a Continuous Improvement System
Prompt: "Teach me how to implement a system of continuous improvement for my productivity. Include strategies like daily reflections, weekly reviews, and tracking key productivity metrics to ensure consistent growth in [specific area]."

ENJOY LEARNING 👍👍

#chatgptprompts

Jupyter notebooks don’t change the world—deployed ML models do.

Here’s how to become unstoppable in the machine learning market

1. Learn programming, ideally Python, from variables and operators to OOP and APIs.

2. Learn basic data manipulation and feature engineering with Numpy and Pandas.

3. Explore supervised and unsupervised machine learning with algorithms like logistic regression, random forest, SVM, XGBoost 2...

4. Dive into deep learning and neural networks. Explore computer vision and NLP

5. Build machine learning pipelines with MLflow and explore the fundamentals of MLOps

6. Start working on end-to-end projects and deploying projects as REST API with Flask or FastAPI

Join for more: https://t.me/machinelearning_deeplearning

Ai tools

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

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