Advanced Diploma in AI Technologies MCQ Book

 Advanced Diploma in AI Technologies MCQ Book

e-Book

Audio Book

e-Book

Advanced Diploma in AI Technologies is a simple e-Book for Engineering Course Advanced Diploma in AI Technologies, Revised Syllabus, It contains objective questions with underlined & bold correct answers MCQ covering all topics including all about the latest & Important about Programming & Data Handling Advanced Python: Focus on object-oriented programming (OOP), decorators, and generators. Data Libraries: Mastery of NumPy (numerical data), Pandas (data manipulation), and Matplotlib/Seaborn (visualization). Data Wrangling: SQL for database management and techniques for cleaning "dirty" data. Machine Learning (Core) Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM). Unsupervised Learning: K-Means Clustering, Principal Component Analysis (PCA), and Anomaly Detection Ensemble Techniques: Bagging and Boosting (XGBoost, CatBoost). Deep Learning & Neural Networks This is the "Advanced" part of the diploma: Neural Network Basics: Perceptrons, Backpropagation, and Activation Functions. Computer Vision: Convolutional Neural Networks (CNNs), Object Detection (YOLO), and Image Segmentation. Natural Language Processing (NLP): Text preprocessing, Sentiment Analysis, LSTMs, and an introduction to Transformers. Modern AI & Deployment (MLOps) Recent programs have added modules on the newest industry trends: Generative AI: Introduction to Large Language Models (LLMs) and Prompt Engineering. Cloud Deployment: Deploying models using AWS, Google Cloud, or Azure. MLOps: Version control for models (DVC), containerization (Docker), and model monitoring. Ethics & Career Skills AI Ethics: Bias detection, fairness in algorithms, and data privacy. Capstone Project: A mandatory real-world project (e.g., building a medical diagnostic tool or a financial fraud detector).