#Bert Model Implementation Guide

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A Text Classification App Using BERT. #instagram #reels #tech #relatable #ai

Comment "AI" to get a list of portfolio projects to do If I were to get started to learn GenAI — here’s exactly how I’d go about it 👇 (No degree, no prior experience needed. Just consistency and curiosity.) Step 1: Learn Python (Weeks 1–4) → Do “Python for Everybody” or freeCodeCamp → Practice daily for 30 minutes → Push everything to GitHub — even your messy code → Start building your proof of work from Day 1 Step 2: Get ML Foundations (Weeks 5–8) → Take Andrew Ng’s ML course (still gold) → Pick 1 project on Kaggle (Titanic is great for starters) → Train a basic model → deploy it with Streamlit → Now you’ve built your first ML app 👏 Step 3: Deep Learning Phase (Months 3–4) → Learn through fast.ai — super hands-on → Fine-tune a small transformer (like BERT) on Hugging Face → Don’t aim for perfection, aim to finish one project well Step 4: Understand Transformers & LLMs (Months 5–6) → Rebuild a mini GPT using Karpathy’s nanoGPT → This will help you actually understand self-attention, tokens, and training loops → Watch explainer videos + read blog posts to reinforce the concepts Step 5: Learn the Real LLM Stack (Months 7–9) → LangChain for chaining prompts & calling tools → LangGraph for multi-agent workflows and memory → Weaviate or LanceDB for retrieval (RAG setups) → QLoRA for fine-tuning open models → vLLM for efficient inference Step 6: Build and Publish (Months 10–12) → Choose one simple use case (like a research assistant or AI chatbot) → Build it end-to-end using the stack you’ve learned → Make a demo video, write a short architecture breakdown → Share on GitHub, LinkedIn, and Twitter — this is your new resume [ai roadmap, llm learning path, genai career, how to learn ai, langchain tutorial, huggingface projects, python for ai, llm from scratch, build in public, ai agent builder, nanoGPT, deep learning 2025, vector dbs, fine tuning llms, ai portfolio project, ml roadmap, data science career guide, prompt engineering, ai learning journey]

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These 10 skills will help you become the top 1% AI expert in the industry If you're looking for a best AI trends post in 2025, this one is the only one you need. ⏳Save this reel for later if you’re in a hurry! Machine Learning - Build predictive models using machine learning, and model training for data-driven decisions. Natural Language Processing (NLP) - Use NLP techniques to make machines understand and generate human language. Proficiency in Large Language Models (LLMs) and Generative AI - Utilizing models like GPT-4, Claude, BERT and others to create human-like text and content across various applications. MLOps - Streamlining AI model deployment and maintenance with CI/CD, monitoring, and automation tools like Kubernates and Docker. Prompt Engineering - Craft effective inputs to guide AI models in generating precise and relevant outputs. Explainable AI (XAI) - Making AI decisions transparent and understandable to build trust and meet regulatory requirements. AI Business Strategy & Implementation - Combine AI with business strategies to drive innovation and operational efficiency. Data Engineering for AI - Designing data pipelines and handling large datasets to ensure data readiness for AI models. AI Ethics - Ensure AI systems are fair, transparent, and responsible, addressing biases and societal impacts. AI-Driven Product Management - Combining AI and product management to develop user-friendly and market-aligned AI solutions. What skill do you think is most important when working with artificial intelligence and LLMs in particular?🧠 Let me know in the comments! ✅P.S. If you want to learn these skills but don’t know where to start, I created an ultra-detailed AI guide for beginners. 🫶🏻Comment artificial and I’ll send you the link. Make sure to follow me on @codingmermaid.ai so I can DM you! Happy Halloween, Danica 🤍🎃

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