SKU/Artículo: AMZ-B0FS83JCHM

Machine Learning: Python for Data Science: A Practical Guide to Building, Training, Testing and Deploying Machine Learning / AI models

Disponibilidad:
En stock

Peso con empaque:
0.52 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Sobre este producto

Foundations of Machine Learning: Begin with a clear definition and exploration of key concepts, tracing the history and evolution of machine learning. Understand the different types—supervised, unsupervised, and reinforcement learning—and discover their real-world applications across finance, healthcare, e-commerce, and more.
End-to-End Workflow: Navigate the complete machine learning pipeline from problem definition and data collection to feature engineering, model training, validation, and iterative improvement. Learn to evaluate model performance with essential metrics and refine your approaches for optimal results.
Essential Python Libraries: Dive deep into essential libraries such as Scikit-Learn, Pandas, and NumPy. Expand your toolkit with advanced tools like XGBoost, CatBoost, TensorFlow Decision Forests, Matplotlib, and Seaborn for robust model building and insightful data visualization.
Advanced Techniques: Master a variety of machine learning techniques including regression, classification, ensemble learning, clustering, dimensionality reduction, and anomaly detection. Each chapter provides practical examples and case studies to reinforce your learning.
Specialized Topics: Explore niche areas such as time series analysis, semi-supervised learning, automating machine learning (AutoML), building recommender systems, and natural language processing (NLP). Gain the skills to tackle diverse and complex data science challenges.
Real-World Applications and Pipelines: Learn to build end-to-end machine learning pipelines, automate workflows with Scikit-learn Pipelines, and deploy your models using Flask or FastAPI. Understand the essentials of monitoring and maintaining deployed models to ensure sustained performance.
Ethical AI Development: Delve into the critical aspects of ethical machine learning. Address bias in datasets and models, ensure transparency and explainability, safeguard privacy and data security, and adhere to guidelines for responsible AI development.

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