Coding_knowledge

✅ Artificial Intelligence (AI) Acronyms You Must Know 🤖🧠 AI → Artificial Intelligence AGI → Artificial General Intelligence ASI → Artificial Superintelligence ML → Machine Learning DL → Deep Learning RL → Reinforcement Learning NLP → Natural Language Processing CV → Computer Vision ASR → Automatic Speech Recognition TTS → Text To Speech LLM → Large Language Model VLM → Vision Language Model MoE → Mixture of Experts ANN → Artificial Neural Network DNN → Deep Neural Network CNN → Convolutional Neural Network RNN → Recurrent Neural Network GAN → Generative Adversarial Network VAE → Variational Autoencoder GNN → Graph Neural Network RAG → Retrieval Augmented Generation LoRA → Low Rank Adaptation PEFT → Parameter Efficient Fine Tuning RLHF → Reinforcement Learning with Human Feedback API → Application Programming Interface SDK → Software Development Kit 💡AI Interview Tip: Interviewers love asking LLM vs traditional ML, RAG vs fine-tuning, and when NOT to use AI in products. 💬 Double Tap ❤️ for more! 🚀

⚠️ AI Mistakes Developers Make ❌ Trusting output blindly ❌ Skipping code review ❌ Using AI instead of thinking ❌ Shipping untested AI code ❌ Ignoring edge cases React ❤️ for more like this #techinfo

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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