Artificial Intelligence

AI is the next biggest skill to learn. AI experts are earing up to $200000+ per year. Here are 4 FREE courses from Google and Microsoft that most people don't know: https://microsoft.github.io/AI-For-Beginners/? https://www.cloudskillsboost.google/paths/118 https://www.deeplearning.ai/courses/ai-for-everyone/ https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ More free resources: https://t.me/udacityfreecourse

Data Science vs Data Engineering vs AI Explained in a song 😂👇 https://youtu.be/WQOzBawrTsQ?si=8wVYA3Me_SGM2GDs

TYPES OF INTELLIGENCE 4 types of Intelligence: 1) Intelligence Quotient (IQ) 2) Emotional Quotient (EQ) 3) Social Quotient (SQ) 4) Adversity Quotient (AQ) 1. Intelligence Quotient (IQ): this is the measure of your level of comprehension. You need IQ to solve maths, memorize things, and recall lessons. 2. Emotional Quotient (EQ): this is the measure of your ability to maintain peace with others, keep to time, be responsible, be honest, respect boundaries, be humble, genuine and considerate. 3. Social Quotient (SQ): this is the measure of your ability to build a network of friends and maintain it over a long period of time. People that have higher EQ and SQ tend to go further in life than those with a high IQ but low EQ and SQ. Most schools capitalize on improving IQ levels while EQ and SQ are played down. Develop their IQ, as well as their EQ, SQ and AQ. They should become multifaceted human beings able to do things independently of their parents. 4. The Adversity Quotient (AQ): The measure of your ability to go through a rough patch in life, and come out of it without losing your mind. When faced with troubles, AQ determines who will give up, who will abandon their family, and who will consider suicide. Parents please expose your children to other areas of life than just Academics. They should adore manual labour (never use work as a form of punishment), Sports and Arts.

3 Data Science Free courses by Microsoft🔥🔥 1. AI For Beginners - https://microsoft.github.io/AI-For-Beginners/ 2. ML For Beginners - https://microsoft.github.io/ML-For-Beginners/#/ 3. Data Science For Beginners - https://github.com/microsoft/Data-Science-For-Beginners Join for more: https://t.me/udacityfreecourse

Master Artificial Intelligence in 10 days with free resources 😄👇 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. Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1 Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience. Free Books and Courses to Learn Artificial Intelligence 👇👇 Introduction to AI Free Udacity Course Introduction to Prolog programming for artificial intelligence Free Book Introduction to AI for Business Free Course Artificial Intelligence: Foundations of Computational Agents Free Book Learn Basics about AI Free Udemy Course (4.4 Star ratings out of 5) Amazing AI Reverse Image Search (4.7 Star ratings out of 5) 13 AI Tools to improve your productivity: https://t.me/crackingthecodinginterview/619 4 AI Certifications for Developers: https://t.me/datasciencefun/1375 Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

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4 AI Certifications for Developers 🔥🔥 1. Intro to AI Ethics https://kaggle.com/learn/intro-to-ai-ethics 2. AI matters https://open.edu/openlearn/education-development/ai-matters/content-section-overview 3. Elements of AI https://course.elementsofai.com 4. Ethics of AI https://ethics-of-ai.mooc.fi

☄️ What is an Artificial Neural Networks?

Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While there’s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.

▶️Content: ➿➿➿➿➿➿➿➿➿➿➿➿ 001 What is Deep Learning 002 Plan of Attack 003 The Neuron 004 The Activation Function 005 How do Neural Networks work 006 How do Neural Networks learn 007 Gradient Descent 008 Stochastic Gradient Descent 009 Back propagation ➿➿➿➿➿➿➿➿➿➿➿➿ Deep Learning Complete Course

You can use ChatGPT to make money online. Here are 10 prompts by ChatGPT 1. Develop Email Newsletters: Make interesting email newsletters to keep audience updated and engaged. Prompt→ "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?" 2. Create Online Course Material: Make detailed and educational online course content. Prompt→ "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?" 3. Ghostwrite eBooks: Use ChatGPT to write eBooks on different topics for online sale. Prompt→ "I want to publish an eBook about healthy eating habits. Can you help me outline and ghostwrite the chapters, focusing on practical tips and easy recipes?" 4. Compose Music Reviews or Critiques: Use ChatGPT to write detailed reviews of music, albums, and artists. Prompt: "I run a music review blog. Can you help me write a detailed review of the latest album by [Artist Name], focusing on their musical style, lyrics, and overall impact?" 5. Develop Mobile App Content: Use ChatGPT to create mobile app content like descriptions, guides, and FAQs. Prompt: "I'm developing a fitness app and need help writing the app description for the store, user instructions, and a list of frequently asked questions." 6. Create Resume Templates: Use ChatGPT to create diverse resume templates for various jobs. Prompt→ "I want to offer a range of professional resume templates on my website. Can you help me create five different templates, each tailored to a specific career field like IT, healthcare, and marketing?" 7. Write Travel Guides: Use ChatGPT to write travel guides with tips and itineraries for different places. Prompt→ "I'm creating a travel blog about European cities. Can you help me write a comprehensive guide for first-time visitors to Paris, including must-see sights, local dining recommendations, and travel tips?" 8. Draft Legal Documents: Use ChatGPT to write basic legal documents like contracts and terms of service. Prompt→ "I need to draft a terms of service document for my new e-commerce website. Can you help me create a draft that covers all necessary legal points in clear language?" 9. Write Video Game Reviews: Use ChatGPT to write engaging video game reviews, covering gameplay and graphics. Prompt→ "I run a gaming blog. Can you help me write a detailed review of the latest [Game Title], focusing on its gameplay mechanics, storyline, and graphics quality?" 10. Develop Personal Branding Materials: Use ChatGPT to help build a personal branding package, including bios, LinkedIn profiles, and website content. Prompt→ "I'm a freelance graphic designer looking to strengthen my personal brand. Can you help me write a compelling biography, update my LinkedIn profile, and create content for my portfolio website?" ENJOY LEARNING 👍👍

To automate your daily tasks using ChatGPT, you can follow these steps: 1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated. 2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically. 3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms. 4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define. 5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy. 6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.

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🧠 ChatGPT For Programming

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/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

🏆 – 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

📈 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

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 (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 👍👍

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