|
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
|
Buy Now
Data Science Solutions with Python - Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn (Paperback, 1st ed.)
Loot Price: R755
Discovery Miles 7 550
You Save: R153
(17%)
|
|
|
Data Science Solutions with Python - Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn (Paperback, 1st ed.)
Expected to ship within 10 - 15 working days
|
Apply 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. What You Will Learn
Understand widespread supervised and unsupervised learning,
including key dimension reduction techniques Know the big data
analytics layers such as data visualization, advanced statistics,
predictive analytics, machine learning, and deep learning Integrate
big data frameworks with a hybrid of machine learning frameworks
and deep learning frameworks Design, build, test, and validate
skilled machine models and deep learning models Optimize model
performance using data transformation, regularization, outlier
remedying, hyperparameter optimization, and data split ratio
alteration Who This Book Is For Data scientists and machine
learning engineers with basic knowledge and understanding of Python
programming, probability theories, and predictive analytics
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
