What You Will Learn Learn how to use the Amazon Machine Learning service from scratch for predictive analytics Gain hands-on experience of key Data Science concepts Solve classic regression and classification problems Run projects programmatically via the command line and the Python SDK Leverage the Amazon Web Service ecosystem to access extended data sources Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations.
Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation.
In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry.
Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development.
This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations.
Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing.
The book is organized as follows. Chapter 1 describes the data life cycle, i. Chapter 2 gets into databases proper, explaining how relational databases organize data.
Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation.
Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries.
It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python.
All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems MySQL and Postgres that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it.
It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
Kelleher Publisher: MIT Press ISBN: Category: Computers Page: View: A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data.
Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance.
It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning.
Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems.
Teaching for Understanding in Mathematics - Ed Southall. Download poems - Gordon S. Download Animal Behavior - Michael D. Download Artemis - Andy Weir. Download Broken Fighter - Maggie Cole. Download Captain America Lives! Omnibus - Ed Brubaker. Download Chainsaw Man, Vol. Download Date with Deceit - Julia Chapman. Download Dear Zoo - Rod Campbell. Download Dragon Ball Super, Vol. Download Friend Request - Laura Marshall.
Download Go the Distance - Jen Calonita. Download Hamnet - Maggie O'Farrell. Download It - Stephen King. Download Jujutsu Kaisen, Vol.
Download Macbeth - William Shakespeare. Download My Hero Academia, Vol. Download Nickelodeon Blue's Clues You! PDF Pok? Girl Stunt Reporters? Read 52 Weeks of Socks: Beautiful patterns for year-round knitting - Laine.
Merino from Cascade Yarns? Read Bella's Butterflies - Sarah Atherton. Read Doctor? Read Dr. Jekyll and Mr. Read Exciting Times - Naoise Dolan. Read Facebook for Dummies - Carolyn Abram. Read Frankenstein - Mary Wollstonecraft Shelley. Read Hamnet - Maggie O'Farrell. Read Haunted - Tony Marturano. Read Inward - Diego P? Read Jews Don? Read Joona Linna 6 - Lars Kepler. Read Layla - Colleen Hoover. Read Monsters - Barry Windsor-Smith. Read One-Punch Man, Vol.
They should match. Append the string ". Your browser will prompt you to download a file, which is the detatched signature associated with the respective distribution. Save the file on your local machine. Verify the signature by running the following command at a command prompt in the directory where you saved signature file and the AWS Data Science Workflows Python SDK installation file.
Both files must be present. If the output includes the phrase BAD signature, check whether you performed the procedure correctly. If you continue to get this response, don't run the installation file that you downloaded previously, and contact AWS Support. Skip to content. Star Branches Tags. Could not load branches. Could not load tags. Latest commit. Fix doc format and links Git stats commits. Are you interested in learning AWS from experts?
Wish to learn more? You will work on various essentials of the AWS cloud platform and create SaaS applications that are scalable, highly available, and fault-tolerant. Step 1. Step 2.
0コメント