AI for Data Scientists - Carter, Thompson
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AI for Data Scientists: Optimize Your Machine Learning Pipelines Supercharge your data science workflows with AI for Data Scientists, a practical guide to optimizing machine learning pipelines for efficiency, scalability, and performance. Whether you're a data scientist, ML engineer, or AI researcher, this book provides actionable insights into automating, fine-tuning, and deploying machine learning models effectively. From feature engineering to hyperparameter tuning, AI for Data Scientists covers cutting-edge techniques and tools to streamline your AI workflows and maximize model…mehr

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AI for Data Scientists: Optimize Your Machine Learning Pipelines Supercharge your data science workflows with AI for Data Scientists, a practical guide to optimizing machine learning pipelines for efficiency, scalability, and performance. Whether you're a data scientist, ML engineer, or AI researcher, this book provides actionable insights into automating, fine-tuning, and deploying machine learning models effectively. From feature engineering to hyperparameter tuning, AI for Data Scientists covers cutting-edge techniques and tools to streamline your AI workflows and maximize model performance. What You'll Learn: * Optimizing Data Pipelines: Automate data cleaning, preprocessing, and transformation for scalable machine learning workflows. * Feature Engineering Strategies: Extract and select the most relevant features to enhance model accuracy and efficiency. * Hyperparameter Tuning: Use techniques like Grid Search, Random Search, and Bayesian Optimization to fine-tune model performance. * Automated Machine Learning (AutoML): Leverage AutoML tools like H2O.ai, Google AutoML, and Auto-Sklearn to streamline model selection and training. * Model Selection & Evaluation: Choose the best algorithms using cross-validation, AUC-ROC curves, and precision-recall metrics. * Parallel and Distributed Computing: Speed up data processing and model training using tools like Dask, Spark, and TensorFlow. * Ensemble Learning: Combine multiple models using techniques like bagging, boosting, and stacking for improved predictive accuracy. * Deep Learning Pipelines: Build and optimize deep learning workflows using TensorFlow, PyTorch, and Keras. * MLOps & CI/CD for ML Models: Implement continuous integration and deployment pipelines for production-ready AI systems. * AI Model Monitoring & Maintenance: Track model performance over time and detect data drift to ensure long-term reliability. * Explainable AI (XAI): Improve model transparency using SHAP, LIME, and other interpretability techniques. * Ethical AI & Bias Mitigation: Identify and reduce algorithmic bias to build fair and responsible AI systems. * Real-World AI Applications: Apply AI-powered solutions to business intelligence, healthcare, finance, and marketing analytics. Who Is This Book For? This book is perfect for data scientists, machine learning engineers, and AI professionals looking to optimize ML workflows, improve model efficiency, and streamline deployment. Why Choose This Book? With its hands-on approach, real-world examples, and deep technical insights, AI for Data Scientists bridges the gap between machine learning theory and practical AI implementation. Start optimizing your machine learning workflows today with AI for Data Scientists: Optimize Your Machine Learning Pipelines-your essential guide to efficient AI model development.
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