LLM Limitations and Learning Through Building

Artificial Intelligence is no longer the future — it is the present. 🤖 The real question is: Are you building AI or just watching it evolve? Mastering AI with Python: Build Your Own Artificial Intelligence Step by Step gives students, developers, and tech enthusiasts a practical roadmap to understand AI and create intelligent systems from scratch using Python. From basic concepts to real AI models, this book turns complex ideas into clear, hands-on learning you can apply immediately.https://https://lnkd.in/dysDw3tW  Don’t just use AI. Build it. Train it. Master it. 📘 Start your AI journey today and become part of the technology shaping tomorrow. #ArtificialIntelligence #Python #MachineLearning #AI #TechEducation #FutureSkills 🚀

🚀 Fake News Detection Project – Progress Update (Day 3) As part of my ongoing Fake News Detection System project, today I focused on text preprocessing, which is an essential step in building effective machine learning models. 🔍 Key activities today: • Understanding text preprocessing techniques • Removing unnecessary words and noise from the dataset • Preparing structured data for model training Proper data preparation plays a critical role in improving the performance and accuracy of machine learning models. This step ensures that the model can better understand patterns within the news data. Each stage of this project is helping me strengthen my skills in Python, data preprocessing, and machine learning concepts while working on a real-world problem. Looking forward to implementing the next phase of the model development. 📈 #MachineLearning #Python #FakeNewsDetection #DataScience #AI #LearningJourney

Recently completed a presentation on Jupyter Notebook for Machine Learning. In this, I covered: Basics and key features of Jupyter Notebook How it helps in building ML models step by step A simple Linear Regression example Data visualization using Python It is a powerful tool for learning, experimenting, and understanding machine learning concepts in a practical way. Looking forward to exploring more in Machine Learning and AI. #MachineLearning #JupyterNotebook #Python #AI #Learning

From learning Python basics to building real-world AI models Anika Mukhopadhyay’s journey with TechBairn’s AI & Machine Learning with Python Program is a true example of transformation — mastering concepts, working on live projects, and gaining confidence to solve real problems. 💡 Hands-on learning 📊 Real-world projects 🎯 Strong mentorship Ready to start your journey in AI & ML? 👉 Visit: https://lnkd.in/gnNCqQ_x 📅 New batch starting from the first week of May #TechBairn #AI #MachineLearning #StudentSuccess #CareerGrowth #Python #Upskill

Are you making these 5 common Machine Learning mistakes? 🤯 Avoid these pitfalls to make your ML projects more accurate, reliable, and professional: 1️⃣ Ignoring Data Cleaning 2️⃣ Overfitting Models 3️⃣ Not Scaling Features 4️⃣ Using Accuracy Alone 5️⃣ Ignoring Feature Importance Small changes = Big impact! 🚀 Which of these mistakes have you seen the most in beginner projects? 👇 #datascience #machinelearning #ai #python #mltips #AIwithHarsha #dataanalytics #techtips

Most people want to build AI. Few learn the logic behind it Welcome to Python for AI Class 03. In this beginner friendly lesson, we cover the core concepts that power decision making in AI: Booleans If–Else Statements Comparison Operators Input Function Logical Operators Because every AI system starts with logic and conditions. Watch the class here: https://lnkd.in/d_gDtw3r What was harder when you started coding syntax or logic? If this helps beginners learn Python for AI step by step, Repost and help someone start their journey.

🚀 Learning AI with Python: My Journey Begins! Artificial Intelligence is no longer the future — it’s the present. And one of the best ways to dive into it is through Python 🐍 Here’s why I started learning AI using Python: ✅ Simple and beginner-friendly syntax ✅ Powerful libraries like NumPy, Pandas, and TensorFlow ✅ Huge community support ✅ Endless real-world applications What I’m focusing on: 🔹 Machine Learning fundamentals 🔹 Data preprocessing & visualization 🔹 Building small AI models 🔹 Exploring deep learning One thing I’ve realized: 👉 Consistency beats intensity. Even 1 hour daily compounds massively over time. If you're thinking about getting into AI, just start. You don’t need to know everything — you just need to take the first step. Let’s grow together in this AI journey 💡 #ArtificialIntelligence #Python #MachineLearning #AI #LearningJourney #TechGrowth #Developers #100DaysOfCode

🚀 Excited to share a project I recently built — Sign Language Recognition System (ASL Alphabet). This system detects hand gestures from a webcam and predicts the corresponding alphabet in real time using a machine learning model. 🔹 Key Highlights • Created a custom dataset (~300–400 images per alphabet) using a webcam • Extracted hand landmark features using MediaPipe • Trained a Random Forest classifier for gesture classification • Implemented real-time alphabet prediction using OpenCV 🔹 Tech Stack Python | OpenCV | MediaPipe | Scikit-learn | Machine Learning 🔹 Future Work • Word detection from continuous gestures • Sentence generation from recognized words This project helped me understand the complete machine learning pipeline — from data collection and feature extraction to model training and real-time inference. 🔗 GitHub Repository: https://lnkd.in/d9-tVJxW #ArtificialIntelligence #MachineLearning #ComputerVision #Python #AIProjects

🔄 Project Update: Student Performance Prediction System Recently I shared my Machine Learning project where I built a system to predict student exam scores using Linear Regression. Today I upgraded the project by adding SHAP (Explainable AI) to make the predictions more interpretable. Now the system not only predicts the score but also explains why the model predicted that score by showing the contribution of each feature such as: • Study Hours • Attendance • Age • Gender This makes the model more transparent and helps understand how different factors influence student performance. Tech used: Python | Scikit-learn | Streamlit | SHAP | Pandas | Matplotlib Still learning and improving 🚀 #MachineLearning #DataScience #ExplainableAI #SHAP #Python #StudentPerformance #MLProjects

There’s a growing shift in AI: optimize inference, not just training. InferScale documentation is a great example of this shift. It enables better LLM outputs using inference-time scaling—making open-source models far more competitive. The docs are concise and actionable, making it easy to experiment and integrate into your stack. If you're building with LLMs, don’t skip this. https://lnkd.in/gRmY5Gc8 #AITrends #LLM #OpenSourceAI #Python #AIEngineering #GenAI #FutureOfAI