000			02294nam a22001937a 4500
005			20240219185318.0
008			240219b |||||||| |||| 00| 0 eng d
020			_a9781484283509
082			_a006.31 _bNOK
100			_aNokeri, Tshepo Chris _914442
245			_aData science solutions with python: _bfast and scalable models using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
260			_bApress _aNew York _c2024
300			_axvi, 119 p.
365			_aINR _b499.00
520			_aApply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. (https://link.springer.com/book/10.1007/978-1-4842-7762-1#about-this-book)
650			_aMachine learning _915068
650			_aArtificial intelligence _913180
942			_cBK _2ddc
999			_c6191 _d6191