Artificial Intelligence

99% of People Use ChatGPT Wrong – Here’s How to Fix It Most people waste ChatGPT’s potential by asking weak questions. Here are 7 expert-level prompts to unlock ChatGPT’s true power 👆

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2025 % of all code written by AI = 50% 2026 % of all code written by AI = 100% This was Anthropic CEO's prediction I say Accurate Prediction 2025 % of all code written by AI = 50% 2026 % of all code written by AI = 100% 2027 % of all code written by AI = 75% 2028 a lot of very very expensive senior developers make bank undoing all the garbage written in 2026 https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

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, especially in linear algebra, probability, and statistics. Here’s what you need to focus on: - Basic probability concepts 🎲 - Inferential statistics 📊 - Regression analysis 📈 - Experimental design & 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 🔍 - Data visualization techniques to visualize variables 📉 - Feature extraction & engineering 🛠️ - Encoding data (different types) 🔐 ⚙️ Machine Learning Fundamentals Use the scikit-learn library along 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 🕹️ Solve 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 📚 In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems. 🕸️ Deep Learning Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled. - CNNs 🖼️ - RNNs 📝 - LSTMs ⏳ 🚀 Machine Learning Project Deployment Machine learning engineers should dive into MLOps and project deployment. Here are the must-have skills: - 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 ENJOY LEARNING 👍👍

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Learn Python & Machine Learning 👆

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 👍👍

Turkey and the EU. Who needs who?  ✔️Turkey has been striving to join the European Union for many years. But the EU is making more and more demands on the country. Ankara has repeatedly asked how long this will last. Asked Turkey and present the entire list of requirements.  " Now Erdogan says that the security of Europe without Turkey is impossible against the background of the weakening of the European Union. The EU is transforming it from a subject of world politics into an object whose future architecture is being worked on by world powers. Turkey is also trying to grab its own piece of the pie in this process." For a long time, Europe kept Turkey at the door, not allowing it to join the European Union. But in the current circumstances, the situation may change." And if Turkey is still accepted in the EU, it will play a dominant role in the new European subsystem." Players like France, Poland, and many other countries will not want this. But in many ways, the possibility or impossibility of Turkey's accession to the EU (as well as the preservation of the EU itself) depends on the United States. ✔️Turkey remains a non-EU entry country. For the European Union – it is a Muslim country with a large number of inhabitants (mostly poor) and a foreign aggressive culture. The absence of the principle of homogeneity also remains valid. Therefore, Turkey will not be accepted in the EU. Speaking about joining the EU, Erdogan has other goals. He sees that there is a sharp rise in right-wing sentiment in Europe, and the European supranational ideology is in a deep crisis. Caricature figures from the European Commission imposed an ultra-liberal agenda on Europe, which led to the strengthening of ultra-right forces. And right-wing Europeans are mostly anti-Muslim. Right-wing Europe will be hostile to Erdogan, so he offers protection to Muslim minorities.  ✔️He says that if Turkey joins the EU, it could solve the problem of labor shortage, give an economic incentive, etc. With him, the Muslims of Europe will receive strong support, and Erdogan at their expense, since these people are also voters, will have an influence on internal European affairs.  Erdogan makes it clear that he will use the levers of pressure he already has on the EU. This is also the gas issue (Erdogan managed to concentrate a significant part of gas transit flows to Europe).And immigration (ability to open / close floodgates for refugees). And in the very distant future, this influence can be converted into Turkey's membership in the EU. #Turkey #EU #Erdogan #Muslim #crisis 🇪🇺 Keep up with the latest Star Union News  🖥

Al Terms Everyone SHOULD KNOW! 1. AGI: Al that can think like humans. 2. CoT (Chain of Thought): Al thinking step-by-step. 3. Al Agents: Autonomous programs that make decisions. 4. Al Wrapper: Simplifies interaction with Al models. 5. Al Alignment: Ensuring Al follows human values. 6. Fine-tuning: Improving Al with specific training data. 7. Hallucination: When Al generates false information. 8. Al Model: A trained system for a task. 9. Chatbot: Al that simulates human conversation. 10. Compute: Processing power for Al models. 11. Computer Vision: Al that understands images and videos. 12. Context: Information Al retains for better responses. 13. Deep Learning: Al learning through layered neural networks. 14. Embedding: Numeric representation of words for Al. 15. Explainability: How Al decisions are understood. 16. Foundation Model: Large Al model adaptable to tasks. 17. Generative Al: Al that creates text, images, etc. 18. GPU: Hardware for fast Al processing. 19. Ground Truth: Verified data Al learns from. 20. Inference: Al making predictions on new data. 21. LLM (Large Language Model): Al trained on vast text data. 22. Machine Learning: Al improving from data experience. 23. MCP (Model Context Protocol): Standard for Al external data access. 24. NLP (Natural Language Processing): Al understanding human language. 25. Neural Network: Al model inspired by the brain. 26. Parameters: Al's internal variables for learning. 27. Prompt Engineering: Crafting inputs to guide Al output. 28. Reasoning Model: Al that follows logical thinking. 29. Reinforcement Learning: Al learning from rewards and penalties. 30. RAG (Retrieval-Augmented Generation): Al combining search with responses. 31. Supervised Learning: Al trained on labeled data. 32. TPU: Google's Al-specialized processor. 33. Tokenization: Breaking text into smaller parts. 34. Training: Teaching Al by adjusting its parameters. 35. Transformer: Al architecture for language processing. 36. Unsupervised Learning: Al finding patterns in unlabeled data. 37. Vibe Coding: Al-assisted coding via natural language prompts.

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5 Handy Tips to Master Data Science ⬇️ 1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel 2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios. 3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases. 4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together. 5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.

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YouTube channels to learn Artificial Intelligence

Artificial Intelligence isn't easy! It’s the cutting-edge field that enables machines to think, learn, and act like humans. To truly master Artificial Intelligence, focus on these key areas: 0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees. 1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques. 2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models. 3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots. 4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics). 5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models. 6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias. 7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications. 8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world. 9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms. Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity. 💡 Embrace the journey of learning and building systems that can reason, understand, and adapt. ⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems! 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 #datascience

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Machine Learning Roadmap 👆

Top 10 machine Learning algorithms 👇👇 1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output. 2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class. 3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure. 4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees. 5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes. 6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set. 7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label. 8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training. 9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors. 10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data. Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊