Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book ? Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more ? Analyze and extract actionable insights from your social data using various Python tools ? A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn ? Understand the basics of social media mining ? Use PyMongo to clean, store, and access data in MongoDB ? Understand user reactions and emotion detection on Facebook ? Perform Twitter sentiment analysis and entity recognition using Python ? Analyze video and campaign performance on YouTube ? Mine popular trends on GitHub and predict the next big technology ? Extract conversational topics on public internet forums ? Analyze user interests on Pinterest ? Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.

Build, Manage and Improve your infrastructure effortlessly. About This Book ? An up-to-date and comprehensive resource on Terraform that lets you quickly and efficiently launch your infrastructure ? Learn how to implement your infrastructure as code and make secure, effective changes to your infrastructure ? Learn to build multi-cloud fault-tolerant systems and simplify the management and orchestration of even the largest scale and most complex cloud infrastructures Who This Book Is For This book is for developers and operators who already have some exposure to working with infrastructure but want to improve their workflow and introduce infrastructure as a code practice. Knowledge of essential Amazon Web Services components (EC2, VPC, IAM) would help contextualize the examples provided. Basic understanding of Jenkins and Shell *s will be helpful for the chapters on the production usage of Terraform. What You Will Learn ? Understand what Infrastructure as Code (IaC) means and why it matters ? Install, configure, and deploy Terraform ? Take full control of your infrastructure in the form of code ? Manage complete infrastructure, starting with a single server and scaling beyond any limits ? Discover a great set of production-ready practices to manage infrastructure ? Set up CI/CD pipelines to test and deliver Terraform stacks ? Construct templates to simplify more complex provisioning tasks In Detail Terraform is a tool used to efficiently build, configure, and improve the production infrastructure. It can manage the existing infrastructure as well as create custom in-house solutions. This book shows you when and how to implement infrastructure as a code practices with Terraform. It covers everything necessary to set up the complete management of infrastructure with Terraform, starting with the basics of using providers and resources. It is a comprehensive guide that begins with very small infrastructure templates and takes you all the way to managing complex systems, all using concrete examples that evolve over the course of the book. The book ends with the complete workflow of managing a production infrastructure as code—this is achieved with the help of version control and continuous integration. The readers will also learn how to combine multiple providers in a single template and manage different code bases with many complex modules. It focuses on how to set up continuous integration for the infrastructure code. The readers will be able to use Terraform to build, change, and combine infrastructure safely and efficiently. Style and approach This book will help and guide you to implement Terraform in your infrastructure. The readers will start by working on very small infrastructure templates and then slowly move on to manage complex systems, all by using concrete examples that will evolve during the course of the book.

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book ? Take your first steps in the world of data science by understanding the tools and techniques of data analysis ? Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods ? Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn ? Learn how to clean your data and ready it for analysis ? Implement the popular clustering and regression methods in Python ? Train efficient machine learning models using decision trees and random forests ? Visualize the results of your analysis using Python’s Matplotlib library ? Use Apache Spark’s MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Build Machine Learning models with a sound statistical understanding. About This Book ? Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. ? Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. ? Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn ? Understand the Statistical and Machine Learning fundamentals necessary to build models ? Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems ? Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages ? Analyze the results and tune the model appropriately to your own predictive goals ? Understand the concepts of required statistics for Machine Learning ? Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models ? Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Use scikit-learn to apply machine learning to real-world problems About This Book ? Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks ? Learn how to build and evaluate performance of efficient models using scikit-learn ? Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn ? Review fundamental concepts such as bias and variance ? Extract features from categorical variables, text, and images ? Predict the values of continuous variables using linear regression and K Nearest Neighbors ? Classify documents and images using logistic regression and support vector machines ? Create ensembles of estimators using bagging and boosting techniques ? Discover hidden structures in data using K-Means clustering ? Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book ? Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts ? Work on a wide array of applications, from simple batch jobs to stream processing and machine learning ? Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn ? Understand object-oriented & functional programming concepts of Scala ? In-depth understanding of Scala collection APIs ? Work with RDD and DataFrame to learn Spark’s core abstractions ? Analysing structured and unstructured data using SparkSQL and GraphX ? Scalable and fault-tolerant streaming application development using Spark structured streaming ? Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML ? Build clustering models to cluster a vast amount of data ? Understand tuning, debugging, and monitoring Spark applications ? Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

When talk show host Dakota Delany agrees to allow bodyguard Ian Russell to shadow her for two weeks, she doesn't count on constantly battling the drop-dead-gorgeous man who monitors her every move. And she definitely doesn't expect the strong and silent Ian to immediately take hold of not only her safety, but also her heart. Strong-willed, feisty Dakota is hardly Ian's ideal client, but she might just be something more–his perfect match. And it would be more than a ratings triumph if the warring opposites put aside their differences and stopped protecting themselves from their shared fear: true love.

When it came to men, definitely the former. But Abby Saunders was certainly aware that when it came to her boss–and his mysterious, possibly unsavory dealings–it could well be the latter. So when she was introduced to crack crime reporter Logan St. John, she was relieved. After all, it was unlikely that the still-grieving, if gorgeous, widower would be looking for a relationship–and maybe he could help her figure out how much trouble she was really in…. As for Logan, his no-strings-attached connection with Abby was about to hit a snag. Oh, he could see how much danger she was in–spotting danger was his area of expertise, after all. Funny how he completely missed the signs around his heart….

A very public proposal! Gorgeous Sicilian Lorenzo Martelli had proposed to Helen in front of his whole family! How could she say anything other than…yes? Now the Big Day was getting alarmingly close–and the bride-to-be was getting cold feet! The groom, however, was as hot-blooded as ever. He wanted Helen, and Lorenzo always got what he wanted. But would he have to wait until she was ready to be his bride–by choice…?

SINGLE…WITH CHILDREN Running from a bad divorce Emily Major was looking for peace, quiet and a safe new life for her son. She was not looking for love, not even from a walking, talking cowboy fantasy like her new neighbor…. But rancher Adam Winchester was used to getting his way, especially when it came to women. With sweet talk and persistence, this hard-as-nails single father was determined to win Emily's heart–come hell, high water or…teenagers. With their kids wreaking havoc on romance, could they be lovers and family? Just how far was Adam willing to go to make the land–and the woman–his?

The Possession of Chase Wheeler… He was a Cheyenne brave with a white man's name. Chase had learned to live between worlds and fight to save his own. But all of his defenses shattered when he saw the very beautiful, very spoiled Letty Sue Withers, whose coquettish charm had brought small-town bachelors to their knees. It was Chase's job to protect her, keep her safe from her own recklessness. Then it came down to his own safety, and Chase was shocked when Letty Sue risked her life for his…and bound him to her forever! Could the willful Letty Sue be the strong, proud woman he had always wanted by his side?

Dearest Godmother, This time I'm making a match for my old school chum–with the man she deserves! If there's ever a socialite who needed her comeuppance, it's personal consultant Parris Hammond. Lucky for me, scruffy but sexy marine biologist Bradford Smith is just the man to give it to her. (He's already learned the only way to tame this shrew is to kiss her speechless!) And now that he's bought a makeover from her, the barbs–and sparks–are flying! Parris knows this frog is more man than she's ever met…but can he truly be her prince? Still a matchmaker, Merry

Could Jolie Randolph be losing her mind? That would explain why the typically focused bank executive had discovered inconsistencies in her usually meticulous work, totally forgotten a business trip and woken up naked in a stranger's bed! Jolie was coming undone and under investigation by the disarmingly handsome Simon Ruhl. But would he bed her or book her? Undercover Colby agent Simon Ruhl had his sights set on Jolie for more than one reason. She was suspected in a money-laundering scheme and she was clearly being set up. Simon was duty bound to protect Jolie from an unknown threat while surreptitiously proving her innocence–all before he went totally insane with desire for her.

Finding a wife… Dr. J. D. Berkley enjoyed his life. He had a good job in ER, a delightful four-year-old son and a truly good friend in nurse Katie Alexander. So why would he need a wife? It wasn't until he wanted to expand the ER that he found being a bachelor could be a stumbling block. So, he had a brilliant idea. Katie could help him find a wife! He couldn't understand why she seemed less than keen on the idea–particularly when he'd made a list of his requirements to make it easier for her….

At her side–day & night… Troubleshooter Matt Crosby has a hidden agenda: he's been hired to find out all he can about Nyssa Blake…. But when Matt meets Nyssa, he falls for her–and his private agenda suddenly gets a lot more personal! Nyssa is naturally wary of Matt, especially as he seems determined to keep her at his side twenty-four hours a day. Tension begins to build between them, and once Nyssa discovers Matt's mission, there's bound to be an explosion!

Stop the wedding Willow and Mike can't decide who's more relieved when each jilts the other at the altar! Having got cold feet, they've now decided to pursue their dream jobs rather than marry…. But, accidentally bumping into Willow afterward, Mike knows they still love one another…and should be facingtheir problems rather than running from them. Somehow, Mike has to capture his runaway bride, convince her they're meant to be together–and this time get her to the church!

Beth Sommers isn't looking for love. She's concentrating on her daughters and her business–and on convincing her ex-husband that their marriage really is over. Even if she was looking, it wouldn't be at a man like Sheriff Jack Murray. She knows Jack's a good cop, a good man…one she can count on. She still figures she'll be better off with a quiet, gentle–maybe even slightly boring–guy. She's already had enough excitement to last her a lifetime. But before long, Jack has her thinking that his kind of excitement is exactly what she needs.

She's not who she says she is! Tory Evans is living her sister's life. Christine is dead, murdered by Tory's vengeful ex-husband, and now–for her own survival–Tory has taken on Christine's identity. Her name, her job, her new home in Shelter Valley, Arizona. He's reinventing his life. Ben Sanders is a divorced father who's come to Shelter Valley to resume the education that was interrupted years before. He's intrigued by one of his professors, the woman he knows as Christine Evans. She's smart, she's beautiful–and she's hiding something. She's also off-limits. Despite that, despite the secrecy and the danger, Tory and Ben are drawn to each other. Second chances really do exist. Especially in a place like Shelter Valley…

NEWS FLASH: TYLER PLAYBOY DATES …A SINGLE MOM?! Rumor has it that our resident attorney Quinn Spencer has been offering Molly Blake more than legal advice these past cold winter nights. Does this sandy-haired stud, who has broken hearts all over Tyler, have his sights set on Molly next? But the sweet owner of the Breakfast Inn Bed comes with a complete package including a four-year-old daughter. Will playboy Quinn be able to resist Molls homespun charm and little Sara's batting baby blues? He says "I won't commit!" but this reporter is taking bets the bachelor is about to fall….

Smart women stay away from cowboys Taking a teaching job in a desolate corner of Idaho was Kate Steele' s way of making sure her in-laws didn' t turn her son, Danny, into a shiftless bronc buster like his father. Except in the middle of nowhere she unexpectedly ran headlong into a genuine buckaroo…. Don' t they? Being a good parent and a born rancher were all that mattered to Ben Trueblood–until gutsy Kate came along. Suddenly he found himself explaining to Danny the rodeo circuit wasn' t all that exciting…and considering the idea of hanging up his spurs. Anything was possible if it meant having Kate. HOME ON THE RANCH Cowboys, ranches, home and family–stories you won' t soon forget.

She's Hiding Something… Well, everyone is hiding something–but Mya Donohue's secret is knocking on the front door, in no uncertain terms. Mya is about to answer to the daughter she'd given birth to nineteen years ago, and Elle has news for her biological mother. Mya is not only a mom, but a grandmother, too. And Elle isn't sticking around for long. She can't. Offering Mya their assistance are her best friends–the only ones who will dare tell her the truth in unmitigated terms; her mother–a woman still sowing her own share of wild oats; and Elle's father–a man of few words, but usually memorable ones, at that. (Note: Mya's current fiancé has conveniently decided to take a long walk.)