There are several AI tools and libraries available to assist with coding in Python. Here are some of the most popular ones:

1. GitHub Copilot: An AI-powered code completion tool developed by GitHub and OpenAI. It can suggest entire lines or blocks of code based on the context of what you're writing.

2. Tabnine: An AI code completion tool that supports various IDEs and code editors. It uses deep learning models to predict and suggest code completions.

3. Kite: An AI-powered code completion and documentation tool that integrates with many popular IDEs. It offers in-line code completions and documentation for Python.

4. PyCharm's Code Completion: JetBrains' PyCharm IDE comes with advanced code completion features, which are enhanced by AI to provide context-aware suggestions.

5. Jupyter Notebooks with AI Integration: Jupyter notebooks can integrate with various AI tools and libraries for code completion and suggestions, like JupyterLab Code Formatter or extensions that integrate with AI models.

6. DeepCode: An AI-based code review tool that helps identify and fix bugs, security vulnerabilities, and code quality issues.

7. IntelliCode: An extension for Visual Studio Code that uses AI to provide code suggestions and improve productivity.

8. Codota: An AI-powered code suggestion tool that integrates with many IDEs and provides context-aware code completions.

9. Repl.it Ghostwriter: An AI-powered code completion tool available in the Repl.it online coding environment.

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How to revolutionize Hollywood with AI.

Unlock new possibilities:

1. Voice Cloning

Clone voices of Hollywood icons:

• Legally clone and use voices with permission.
• Recreate iconic voices for new projects.
• Preserve legendary performances for future generations.

2. Custom Voices

Create unique voices for your projects:

• Generate up to 20 seconds of dialogue.
• Select from preset voice options or create your own.

3. Lip Sync Tool

Bring still characters to life:

• Use ElevenLabs's Lip Sync tool.
• Select a face and add a script.
• Generate videos with synchronized lip movements.

AI is reshaping the industry, voice cloning is part of a broader trend.

Filmmakers can now recreate voices of iconic actors.

AI is transforming healthcare through various applications that enhance patient care, streamline operations, and improve diagnostics and treatment outcomes. Here are some key applications of AI in healthcare:

1. Medical Imaging and Diagnostics:
- Image Analysis: AI algorithms analyze medical images (X-rays, MRIs, CT scans) to detect abnormalities such as tumors, fractures, and infections.
- Disease Detection: AI systems help in early detection of diseases like cancer, diabetic retinopathy, and cardiovascular conditions.

2. Predictive Analytics:
- Patient Risk Assessment: AI models predict patient risks for conditions like sepsis, heart attacks, and hospital readmissions based on electronic health records (EHRs) and other data.
- Population Health Management: AI analyzes large datasets to identify public health trends and predict outbreaks.

3. Personalized Medicine:
- Treatment Recommendations: AI helps tailor treatment plans based on individual patient data, including genetics, lifestyle, and response to previous treatments.
- Drug Discovery: AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy and safety.

4. Virtual Health Assistants and Chatbots:
- Symptom Checking: AI-powered chatbots provide preliminary diagnosis and advice based on reported symptoms.
- Patient Engagement: Virtual assistants remind patients to take medications, schedule appointments, and follow post-treatment care plans.

5. Robotic Surgery:
- Surgical Assistance: AI-driven robots assist surgeons with precise and minimally invasive procedures, enhancing accuracy and reducing recovery times.
- Autonomous Surgery: Research is ongoing into fully autonomous surgical robots for specific procedures.

6. Administrative Workflow Automation:
- Medical Coding and Billing: AI automates coding and billing processes, reducing errors and administrative burdens.
- EHR Management: AI helps manage and update electronic health records, ensuring accurate and up-to-date patient information.

7. Clinical Decision Support Systems (CDSS):
- Decision Making: AI supports healthcare providers with evidence-based recommendations, improving diagnosis and treatment decisions.
- Error Reduction: CDSS helps reduce medical errors by cross-referencing patient data with clinical guidelines.

8. Remote Monitoring and Telehealth:
- Wearable Devices: AI analyzes data from wearable devices to monitor patient health in real-time, alerting healthcare providers to potential issues.
- Telemedicine: AI enhances telehealth platforms, providing real-time analysis and support during virtual consultations.

9. Natural Language Processing (NLP):
- Clinical Documentation: AI-powered NLP systems transcribe and analyze clinical notes, making it easier to extract relevant information.
- Voice Assistants: AI voice assistants help doctors with hands-free data entry and information retrieval during patient consultations.

10. Mental Health Support:
- Chatbots for Therapy: AI chatbots provide cognitive behavioral therapy (CBT) and other support to individuals with mental health conditions.
- Emotion Detection: AI analyzes speech and text to detect emotional states, providing insights for mental health professionals.

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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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ChatGPT Prompt to learn any skill
👇👇
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)

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.

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 👍👍

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 😊

🏆 – 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

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.

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👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5

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All the best 👍👍