“…What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. …”

“…Learn who does what, when they do it, and how it's done Discover the many fields involved in crime scene investigation Understand what really happens inside a forensics lab Examine famous forensics cases more intriguing than any TV show Forensic scientists work in a variety of environments and in many different capacities. …”

“…What you will learn Carry out an essential review of re-sampling methods, bootstrap, and jackknife Explore the key ensemble methods: bagging, random forests, and boosting Use multiple algorithms to make strong predictive models Enjoy a comprehensive treatment of boosting methods Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use the supplied R code to implement ensemble methods Learn stacking method to combine heterogeneous machine learning models Who this book is for This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. …”

“…Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. …”

“…What you will learn Simplify your feature engineering pipelines with powerful Python packages Get to grips with imputing missing values Encode categorical variables with a wide set of techniques Extract insights from text quickly and effortlessly Develop features from transactional data and time series data Derive new features by combining existing variables Understand how to transform, discretize, and scale your variables Create informative variables from date and time Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. …”

“…This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL. …”

“…What you will learn Obtain, verify, clean and transform text data into a correct format for use Use methods such as tokenization and stemming for text extraction Develop a classifier to classify comments in Wikipedia articles Collect data from open websites with the help of web scraping Train a model to detect topics in a set of documents using topic modeling Discover techniques to represent text as word and document vectors Who this book is for This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. …”

“…By the end, you'll be able to execute sophisticated and meaningful data science experiments - why not become a citizen scientist and make a real contribution to the fight against climate change. …”

“…What you will learn Develop analytical thinking to precisely identify a business problem Wrangle data with dplyr, tidyr, and reshape2 Visualize data with ggplot2 Validate your supervised machine learning model using k-fold Optimize hyperparameters with grid and random search, and Bayesian optimization Deploy your model on Amazon Web Services (AWS) Lambda with plumber Improve your model's performance with feature selection and dimensionality reduction Who this book is for This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. …”

“…What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter's streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. …”

“…Audience This course is ideal for Software engineers, aspiring data engineers or data analysts, and data scientists who want to excel in their careers in the IT domain. …”

“…Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. …”

“…Modular chapters designed to support introductory courses of any length Introductions to unit conversions, basis selection, and process measurements Strategies for solving diverse material and energy balance problems, including material balances with chemical reaction and for multi-unit processes, and energy balances with reaction Clear introductions to key concepts ranging from stoichiometry to enthalpy Coverage of ideal/real gases, multi-phase equilibria, unsteady-state material, humidity (psychrometric) charts, and more Self-assessment questions to help readers identify areas they don't fully understand Thought, discussion, and homework problems in every chapter New biotech, bioengineering, nanotechnology, green/environmental engineering, and process safety coverage Relevant new MATLAB and Python homework problems and projects Extensive tables, charts, and glossaries in each chapter Reference appendices presenting atomic weights and numbers, Pitzer Z0/Z1 factors, heats of formation and combustion, and more Easier than ever to use, this book is the definitive practical introduction for students, license candidates, practicing engineers, and scientists…”

“…A deep knowledge of the genomic diversity of Xanthomonas spp. is required for scientists to properly identify strains, to help preventing future disease outbreaks and to achieve knowledge-informed sustainable disease resistance in crops. …”

“…What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. …”

“…It opens with a brilliant Introduction by the writer and political scientist Manuel Arias Maldonado, in which he defends the relevance of this text in our days…”

“…Whether for the veteran computer engineer working in the field, other computer scientists and professionals, or for the student, this is a one-stop-shop for quantum computing in cyber security and a must have for any library…”

“…The PMO as the learning engine for the enterprise and the changing role of the program offices. Becoming a Change Scientist, the maturing of value and strategic metrics in programs, and how to achieve the right metrics design and mix. …”

“…This volume is a vital read for critical geographers, anthropologists, and political scientists, as well as scholars of tourism and cultural studies. …”

“…Shaan Khosla is a data scientist at Nebula where he researches, designs, and develops NLP models. …”