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🚀 Excited to share my latest project: a Heart Attack Prediction Model! 🚑💓 This project harnesses machine learning to predict the risk of heart attacks based on individual health data. By identifying high-risk individuals early, this model aims to enable timely medical interventions that could save lives. Key Achievements: High Accuracy: Achieved an impressive accuracy of 88.52%, demonstrating the model's effectiveness. Machine Learning Algorithm: Utilized robust classification techniques that balance precision and recall, as evident in the detailed classification report:Precision: High precision rates (0.89 for class 0 and 0.88 for class 1) indicate the model's reliability in identifying true positives. Recall: Strong recall scores (0.86 for class 0 and 0.91 for class 1) highlight its capability to capture the majority of relevant cases. F1-Score: Balanced F1-scores (0.88 for class 0 and 0.89 for class 1) reflect the model's consistent performance across both classes. Explainable AI with LIME: To ensure transparency and trustworthiness in healthcare applications, I integrated LIME (Local Interpretable Model-agnostic Explanations) to explain predictions. This tool helps illuminate why the model predicts certain individuals to be at higher risk, making AI decisions transparent and understandable for healthcare providers. OpenAI Integration using GPT-4: Incorporated OpenAI’s GPT-4 to handle complex natural language processing tasks, such as interpreting medical notes and providing detailed, understandable explanations of the model's findings to healthcare professionals. https://lnkd.in/dgfriW87 #DataScience #MachineLearning #AI #BigData #Analytics #ArtificialIntelligence #DeepLearning #Tech #Innovation #HealthTech #HealthcareInnovation #PreventiveMedicine #Python #Technology #DigitalHealth #AIinHealthcare #DataAnalytics #DataVisualization #AIForGood #MachineLearningAI #PredictiveAnalytics #Bioinformatics #MLops #DataDriven #SmartHealthcare #DataForGood #HealthData #AnalyticsInHealth #AIResearch #GenAI #LLM

🚀 Unlock the Power of AI: Master Prompt Engineering! 🧠 Are you ready to take your AI interactions to the next level? 📈 I've just published a comprehensive guide on "Mastering AI Prompt Engineering: From Basics to Advanced Techniques" that you won't want to miss! In this blog post, I break down 10 essential prompt engineering methods, ranging from beginner-friendly to advanced techniques. Here's a sneak peek: 1️⃣ Direct Querying: The foundation of AI interaction 2️⃣ Contextual Framing: Setting the stage for nuanced responses 3️⃣ Exemplar-Based Prompting: Teaching AI by example 4️⃣ Persona Adoption: Unlocking role-specific insights 5️⃣ Format Specification: Structuring AI outputs 6️⃣ Tone Modulation: Tailoring communication styles 7️⃣ Chain of Thought (CoT): Step-by-step problem solving 8️⃣ Tree of Thoughts (ToT): Exploring multiple solution paths 9️⃣ Iterative Refinement: Perfecting AI-generated content 🔟 Prompt Chaining: Tackling complex, multi-faceted tasks Whether you're a developer, researcher, or AI enthusiast, this guide offers practical tips and real-world examples to enhance your AI prompting skills. 💡 Ready to revolutionize your AI interactions? Check out the full blog post here: https://lnkd.in/dDwuscc3 #ArtificialIntelligence #PromptEngineering #AITips #TechInnovation #MachineLearning #LLMs What's your favourite prompt engineering technique? Share in the comments below! 👇

🚀 New Blog Alert! 🚀 I'm thrilled to announce my latest blog post: "Tricks for Giving the Right Prompts to Use AI Tools Effectively for Devs" 📈🤖 Unleash the true potential of AI in your development workflow! Dive into this comprehensive guide where I reveal key strategies to communicate with AI tools like a pro. Whether you're aiming to boost productivity, achieve precise results, or master AI interactions, this blog has got you covered! ✨ What's Inside: 1. Master the Art of AI Prompts: Learn to craft prompts that yield accurate and useful responses. 2. Supercharge Your Workflow: Discover how AI can enhance your productivity. 3. Avoid Common Pitfalls: Identify and steer clear of typical mistakes. 4. Real-World Examples: Get inspired by practical applications and best practices. Don’t miss out on these essential tips to elevate your AI game. Your feedback and thoughts are highly appreciated! 🌐 Read the Full Article https://lnkd.in/gRqJ9mBR #AI #ArtificialIntelligence #MachineLearning #DevCommunity #TechTips #AIforDevs #Programming #SoftwareDevelopment #PromptEngineering

🚀 Exciting News from TecAce! 🚀 We're thrilled to share our latest case study showcasing a more intelligent and systematic approach to document summarization. 📄✨ 🔍 Case Study: Enhancing Document Summarization with AI In our constant quest to enhance productivity and efficiency, TecAce has developed a groundbreaking method for summarizing complex documents. This new approach not only simplifies content but also retains critical information, making it easier than ever to digest extensive materials quickly. 🌟 Highlights: AI-Driven Summaries: Leveraging advanced algorithms to ensure precision and context retention. Efficiency at Scale: Dramatically reduce reading time without losing essence. Cross-Industry Applications: From academic papers to extensive legal documents, our solution is versatile. 👀 Read the full story on how AssistAce is changing the game in document management and what this could mean for your industry: https://lnkd.in/gMBa7wxH Let’s discuss! How do you currently handle document summarization in your organization? Could AI-enhanced methods streamline your workflows? #AI #MachineLearning #DocumentSummarization #TechInnovation #AssistAce

#GPT-o1 vs. #GPT-4o: Which #AI Model is Right for You? OpenAI’s new GPT-o1 model isn’t better than GPT-4o—it’s just built for different things. Here’s the breakdown: 🧠 GPT-o1 is a game-changer for complex tasks like math, logical reasoning, and temporal understanding. It’s designed to think through problems step-by-step—things that most AI models struggle with. But here’s the catch: GPT-o1 isn’t the best at everything! For tasks like code completion or creative writing, GPT-4o and other models might actually do better. In fact, on certain benchmarks, GPT-o1 ranks behind GPT-4o and even Claude-3.5 Sonnet! 💡 Why? Because GPT-o1 takes more time (and money) to give you a well-reasoned answer. It generates more output called “reasoning tokens” to arrive at its conclusions. You probably don’t need that if you’re just having a casual conversation or simple chatbot interaction. Where GPT-o1 really shines is in complex scenarios like: “Here’s everything I know, analyze it carefully, and when you’re ready, give me a thorough answer and tell me why.” As AI gets more advanced, knowing which model to use for different tasks will be a key skill in business. GPT-o1 may not be the right tool for every job, but in the right context, it’s a total game changer. #AIInnovation #OpenAI #GPTModels #BusinessIntelligence #AIReasoning #TechTrends #AIForBusiness #FutureOfWork #DataProcessing #AIGameChanger

I was thinking recently on how to talk with customers in #industry about Machine Learning. Two main points they misunderstand the most from my experience: 1️⃣ ML projects are not just software, you cannot simply copypaste existing solutions. You also need data, and even more importantly, quality data. 2️⃣ ML development is a continuous process, prepare to spend resources even after deploying the first viable model. Both things needs to be explained to stakeholders as soon as possible to avoid unrealistic expectations. #ai #artificialintelligence #machinelearning #communication

🤓 100+ hours of work and 𝟏𝟎𝟎 𝐀𝐈 𝐓𝐞𝐫𝐦𝐬 that are being used in the artificial intelligence domain a glance at some terms with short definitions, you will find the complete list with a bit more detailed definitions in the document; - 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Learning via feedback loops. - 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐃𝐞𝐬𝐜𝐞𝐧𝐭: Optimizing loss function. - 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏): Processing human language data. - 𝐂𝐡𝐚𝐭𝐛𝐨𝐭 Automated conversational agent. - 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐆𝐀𝐍): Competitive content generation models. - 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Decentralized machine learning. - 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 (𝐒𝐕𝐌): Classifying complex data. - 𝐀𝐮𝐭𝐨𝐞𝐧𝐜𝐨𝐝𝐞𝐫𝐬: Unsupervised data encoding. - 𝐅𝐞𝐰-𝐒𝐡𝐨𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Learning from few examples. - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐢𝐨𝐧: Translating languages using AI. - 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐆𝐈): Advanced, versatile AI. - 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Layered neural network architectures. - 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥𝐢𝐭𝐲: Integrating multiple data types. - 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬: Probabilistic graphical models. - 𝐈𝐦𝐚𝐠𝐞 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧: Identifying objects in images. - 𝐍𝐞𝐮𝐫𝐚𝐥 𝐑𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐅𝐢𝐞𝐥𝐝𝐬 (𝐍𝐞𝐑𝐅): 3D scene representation. - 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 (𝐗𝐀𝐈): Understandable AI decisions. - 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Locating objects in images. ................and read the document for more👇🏻 #happylearning #futurewithai #aiterms #artificialintelligence #machinelearning #promptengineering

Accelerate Model Tuning with Knowledge Distillation: A Practical Guide Knowledge Distillation is a pivotal technique in optimizing AI models, enabling the transfer of insights from a large-scale model (teacher) to a more compact and efficient model (student). This approach is crucial for deploying resource-friendly models in production without compromising on performance. Practical Applications: 🔸 Custom Fine-Tuning of Language Models (LLMs):   1. Define specific tasks or behaviors for your LLM.   2. Develop prompt templates that encapsulate these behaviors.   3. Utilize APIs from robust models like GPT or Gemini.   4. Provide a few-shot learning framework to refine model outputs.   5. Generate a tailored dataset entirely from your large LLMs to optimize your model’s training. 🔹 Pre-annotation for Object Detection/Segmentation:   1. Extract samples directly from your raw dataset.   2. Prepare and configure the general-purpose model for initial processing.   3. Run samples through sophisticated models like Facebook SAM or YOLO X for precise object detection and segmentation.   4. Streamline the annotation process tailored to your specific needs.   5. Conduct quality assurance on your refined dataset.   6. Version and manage your dataset for optimal training outcomes. Key Takeaways: - Use large models to streamline the training of smaller, specialized models. - Generate and refine custom datasets for fine-tuning LLMs and vision models efficiently. - Begin model training without manual sample preparation to save time and resources. Stay Updated: For more insightful guides and tips in the realms of #MachineLearning, #ComputerVision, #MLOps, and #GenerativeAI, make sure to follow my updates #HamzaAliKhalid. 🔔 Hit the follow button for regular updates and feel free to share your experiences or questions in the comments! #AI #DataScience #ArtificialIntelligence #TechInnovation #HamzaAliKhalid #MoonSys

Can You Speak AI? Master the Art of Prompting and Transform Your Tech Career Ever felt lost in translation when communicating with AI? You're not alone. As a self-taught prompt engineer, I've cracked the code to effective AI communication. In my recent "Mastering the Art of AI Prompting" workshop, I shared insights that bridge the gap between human intent and AI output. Here's a glimpse into what we covered: • The power of meta-prompting: Creating reusable templates that leverage advanced techniques • Ethical AI interaction: Ensuring responsible and unbiased outputs • From novice to AI whisperer: Techniques like few-shot learning and chain-of-thought prompting Our unique "Meta-Meta-Prompt" approach is a game-changer. I have used it to generate a complex data analysis prompt, cutting my personal task completion time by 50%! Ready to elevate your AI communication skills? Let's connect! Share your biggest AI interaction challenge in the comments, or DM me for details on upcoming workshops. https://lnkd.in/eJNyYmq8 #AIPromptEngineering #TechSkills #FutureOfWork #ArtificialIntelligence #ContinuousLearning