Coding_knowledge

IIT Roorkee has launched its Python certification course, in which Python will be taught from basic to advanced level by IIT professors. If you want to study from IIT professors, book your test now. 👇 Book Now :- https://www.masaischool.com/our-program/mit-idss/ml-python-dl?utm_source=scttest&utm_medium=e&utm_campaign=sct26

Hey folks, IIT Roorkee has launched its AI and Data Science certification course. You can get access by passing a ₹99 test, and the course will be taught live by IIT professors. So if you can pass the test and want to learn live from IIT professors, book the test using the link given below. The test will be held on 8th February. Certification in Data Science and Artificial Intelligence Program from E&ICT, IIT Roorkee 🚀 : https://www.masaischool.com/program/iit-roorkee-eict/ai-ds?utm_source=scttest&utm_medium=e&utm_campaign=sct26

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✅ Complete Roadmap to Master Artificial Intelligence in 3 Months Month 1: Foundations - Week 1: AI basics - What artificial intelligence is - AI vs machine learning vs deep learning - Real business use cases Outcome: You know where AI fits in real products. - Week 2: Math and logic essentials - Linear algebra basics, vectors, matrices - Probability and statistics basics - Cost functions and optimization idea Outcome: You understand how models learn. - Week 3: Python for AI - Python syntax for analysis - NumPy arrays and operations - Pandas for data handling Outcome: You work with data confidently. - Week 4: Data preparation - Data cleaning and preprocessing - Handling missing values and outliers - Feature selection basics Outcome: Your data is model ready. Month 2: Machine Learning Core - Week 5: Supervised learning - Linear and logistic regression - Decision trees and random forest - Model evaluation, accuracy, precision, recall Outcome: You build prediction models. - Week 6: Unsupervised learning - K-means clustering - Hierarchical clustering - PCA with real examples Outcome: You find patterns in data. - Week 7: Model improvement - Overfitting and underfitting - Cross validation - Hyperparameter tuning Outcome: Your models perform better. - Week 8: Intro to deep learning - Neural network basics - Activation functions - Backpropagation concept Outcome: You understand how deep models work. Month 3: Applied AI and Job Prep - Week 9: Deep learning tools - TensorFlow or PyTorch basics - Build a simple neural network - Train and test models Outcome: You build neural models. - Week 10: Real world AI project - Choose use case, spam detection or sales prediction - Data prep, model training, evaluation - Simple deployment demo Outcome: One strong AI project. - Week 11: Interview preparation - Machine learning theory questions - Model selection questions - Project explanation flow Outcome: You answer with clarity. - Week 12: Resume and practice - AI focused resume - GitHub with notebooks and projects - Daily problem solving Outcome: You are AI job ready. Practice platforms: Kaggle, Google Colab, Scikit-learn docs Double Tap ♥️ For Detailed Explanation of Each Topic

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✅ Generative AI Interview Prep Guide ✨🧠 Want to crack roles in GenAI (Prompt Engineer, LLM Developer, Applied Scientist)? Here's what to focus on: 1️⃣ What is Generative AI? • AI that *creates* text, images, audio, or code • Powered by deep learning models (e.g., GPT, DALL·E, Midjourney) • Examples: ChatGPT, Claude, Bard, GitHub Copilot 2️⃣ Core Concepts to Understand • Language Models: GPT, BERT, LLaMA • Transformers: self-attention, positional encoding • Tokenization: BPE, WordPiece • Embeddings: how meaning is captured in vector space • Fine-tuning vs Prompt Engineering vs Retrieval-Augmented Generation (RAG) 3️⃣ Prompt Engineering Basics • Zero-shot, few-shot prompting • Chain of Thought (CoT) prompting • System vs user prompts (in APIs) • Prompt templates for summarization, Q&A, code generation 4️⃣ Key Tools & Frameworks • OpenAI API / Anthropic / Cohere • LangChain, LlamaIndex (RAG tools) • Hugging Face Transformers • Vector DBs: Pinecone, FAISS, Chroma 5️⃣ Must-Know Projects • AI chatbot with memory (LangChain + OpenAI) • PDF Q&A bot using RAG • Blog/article summarizer • AI code reviewer • Voice-to-text-to-response assistant (using Whisper + GPT) 6️⃣ Interview Questions to Prepare • How do LLMs understand context? • What is token limit & why does it matter? • Difference between RAG and fine-tuning? • What are hallucinations in LLMs? • How would you evaluate a GenAI system? 7️⃣ Practice & Learn From • OpenAI Cookbook • LangChain docs • Hugging Face Spaces • Build and share on GitHub 8️⃣ Pro Tips ✔️ Know how to reduce hallucinations ✔️ Build mini tools to showcase skills ✔️ Stay updated on new models & benchmarks ✔️ Prepare case studies for product thinking 💬 Tap ❤️ for more!

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🌐 Top Python Libraries for Web Development 🐍💻 Python isn’t just for data science — it's also a powerful tool for building modern, scalable web apps! 🧱 Web Frameworks: • Django — High-level framework for fast, secure, full-stack web apps • Flask — Lightweight & flexible micro-framework, perfect for APIs • FastAPI — Modern, fast (high-performance) framework for building APIs with Python 3.7+ 📦 Backend Utilities: • SQLAlchemy — Powerful ORM (Object Relational Mapper) for databases • Jinja2 — Templating engine for rendering HTML from Python • Celery — Task queue for running background jobs (e.g., email, scheduling) 🔐 Security & Auth: • Authlib — Easy OAuth and JWT integration • Passlib — Password hashing and authentication tools 💻 Frontend Integration: • WTForms — Form handling & validation • Flask-WTF — Seamless form support for Flask apps 💡 Learn these to start building real-world web applications with Python! 💬 Tap ❤️ for more!

Hey folks, if you’re failing SQL interviews again and again, then memorize these 50 SQL interview questions and answers—you’ll clear your SQL interview in no time 👇 https://www.linkedin.com/posts/harish-bhatt-449ab2264_most-asked-sql-interview-queries-in-maang-activity-7417792076043587584-uYIE?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEDyQvkBZoHj8lu2sM00x1Dhc54Sh0qPmOA