Search results for "Health Analytics with Python"
Items we need to order for you
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsAbout This Book• Design, engineer and deploy scalable machine learning solutions with the power of Python• Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is ForThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.What You Will Learn• Apply the most scalable machine learning algorithms • Work with modern state-of-the-art large-scale machine learning techniques• Increase predictive accuracy with deep learning and scalable data-handling techniques• Improve your work by combining the MapReduce framework with Spark• Build powerful ensembles at scale• Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machineIn DetailLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.Style and approachThis efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
R 1,889.00
R 1,343.00
R 611.00
R 611.00
R 611.00
R 611.00
R 611.00
R 336.00
R 611.00
Hands-On Graph Neural Networks Using Python
Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learnUnderstand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is forThis book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
R 1,933.00
Learning scikit-learn: Machine Learning in Python
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
R 977.00
R 611.00
