ISBN: 9798309675623 - real-world ml projects: hands-on learning: 6 (ai from scratch : step-by-step guide to mastering artificial intelligence) (9 risultati)

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Paperback. Condizione: new. Paperback. Machine learning is revolutionizing industries, but understanding theory alone isn't enough-you need hands-on experience to excel in the real world. Real-World ML Projects: Hands-on Learning is the ultimate guide to mastering machine learning through practical, end-to-end projects designed to solve real business problems. This book, the sixth installment in the "AI from Scratch: Step-by-Step Guide to Mastering Artificial Intelligence" series, provides a structured, project-based approach to learning ML. Instead of just reading about algorithms, you'll work on industry-grade machine learning projects, covering everything from data collection and model training to deployment and scaling. What You'll Learn: Data Preparation & Feature Engineering - Learn how to clean, preprocess, and engineer features from real-world datasets.Supervised & Unsupervised Learning - Implement regression, classification, clustering, and anomaly detection models.Deep Learning & NLP - Train CNNs for image recognition and build transformer-based NLP models.MLOps & Model Deployment - Use Flask, Docker, Kubernetes, and cloud platforms to deploy scalable ML models.10 Real-World Projects - Solve industry-specific problems in finance, healthcare, retail, NLP, and Edge AI.Hands-On Machine Learning Projects Include: Predicting House Prices - Train regression models and deploy a web-based prediction tool.Customer Churn Prediction - Analyze customer behavior and build a churn detection model.Sentiment Analysis on Social Media - Implement NLP techniques to classify positive and negative sentiments.Fraud Detection in Financial Transactions - Use anomaly detection to identify fraudulent activities.Demand Forecasting for Retail - Build time-series forecasting models to optimize inventory.Image Classification with CNNs - Train a deep learning model to classify images with high accuracy.Recommender System for E-Commerce - Implement collaborative and content-based filtering.Chatbot with NLP - Build and deploy a conversational AI chatbot.Autonomous Vehicle Simulation - Train reinforcement learning models for self-driving cars.Edge AI with TinyML - Deploy ML models on microcontrollers like Raspberry Pi and Arduino.Why This Book?Hands-On Approach - Learn by doing with step-by-step guidance and real datasets.Industry-Relevant Skills - Gain experience with tools like TensorFlow, PyTorch, Scikit-Learn, and more.Deployment Focus - Move beyond theory and learn to operationalize ML models.Cutting-Edge Topics - Explore AutoML, Large Language Models (LLMs), and Generative AI.Practical Code Examples - Get fully functional Python code for all projects.Who Should Read This Book?Aspiring Data Scientists & ML Engineers - Build a portfolio with real-world projects.Software Developers - Learn to integrate machine learning into applications.Business Analysts & AI Enthusiasts - Apply ML to solve practical problems.Students & Researchers - Get hands-on experience with industry-standard ML techniques.Turn Your Knowledge into Action! By the end of this book, you'll have built a portfolio of real-world ML projects, mastered the ML workflow, and gained the confidence to deploy models in production. If you're ready to bridge the gap between theory and practice, this book is your roadmap to becoming an industry-ready machine learning expert. Start building real Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.