Kyle interviews Julia Silge about her path into data science, her book Text Mining with R, and some of the ways in which she's used natural language processing in projects both personal and professional. Related Links https://stack-survey-2018.glitch.me/ https://stackoverflow.blog/2017/03/28/realistic-developer-fiction/

One of the most challenging NLP tasks is natural language understanding and reasoning. How can we construct algorithms that are able to achieve human level understanding of text and be able to answer general questions about it? This is truly an open problem, and one with the bAbI dataset has been constructed to facilitate. bAbI presents a variety of different language understanding and reasoning tasks and exists as benchmark for comparing approaches. In this episode, Kyle talks to Rasmus Berg Palm about his recent paper Recurrent Relational Networks

The earliest efforts to apply machine learning to natural language tended to convert every token (every word, more or less) into a unique feature. While techniques like stemming may have cut the number of unique tokens down, researchers always had to face a problem that was highly dimensional. Naive Bayes algorithm was celebrated in NLP applications because of its ability to efficiently process highly dimensional data. Of course, other algorithms were applied to natural language tasks as well. While different algorithms had different strengths and weaknesses to different NLP problems, an early paper titled Scaling to Very Very Large Corpora for Natural Language Disambiguation popularized one somewhat surprising idea. For many NLP tasks, simply providing a large corpus of examples not only improved accuracy, but it also showed that asymptotically, some algorithms yielded more improvement from working on very, very large corpora. Although not explicitly in about NLP, the noteworthy paper The Unreasonable Effectiveness of Data emphasizes this point further while paying homage to the classic treatise The Unreasonable Effectiveness of Mathematics in the Natural Sciences. In this episode, Kyle shares a few thoughts along these lines with Linh Da. The discussion winds up with a brief introduction to Zipf's law. When applied to natural language, Zipf's law states that the frequency of any given word in a corpus (regardless of language) will be proportional to its rank in the frequency table.

This episode reboots our podcast with the theme of Natural Language Processing for the next few months. We begin with introductions of Yoshi and Linh Da and then get into a broad discussion about natural language processing: what it is, what some of the classic problems are, and just a bit on approaches. Finishing out the show is an interview with Lucy Park about her work on the KoNLPy library for Korean NLP in Python. If you want to share your NLP project, please join our Slack channel.  We're eager to see what listeners are working on! http://konlpy.org/en/latest/    

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading thisbook, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. What You Will Learn Build a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model Who This Book Is For Data science and machine learning professionals.

Today's spam filters are advanced data driven tools. They rely on a variety of techniques to effectively and often seamlessly filter out junk email from good email. Whitelists, blacklists, traffic analysis, network analysis, and a variety of other tools are probably employed by most major players in this area. Naturally content analysis can be an especially powerful tool for detecting spam. Given the binary nature of the problem ( or ) its clear that this is a great problem to use machine learning to solve. In order to apply machine learning, you first need a labelled training set. Thankfully, many standard corpora of labelled spam data are readily available. Further, if you're working for a company with a spam filtering problem, often asking users to self-moderate or flag things as spam can be an effective way to generate a large amount of labels for "free". With a labeled dataset in hand, a data scientist working on spam filtering must next do feature engineering. This should be done with consideration of the algorithm that will be used. The Naive Bayesian Classifer has been a popular choice for detecting spam because it tends to perform pretty well on high dimensional data, unlike a lot of other ML algorithms. It also is very efficient to compute, making it possible to train a per-user Classifier if one wished to. While we might do some basic NLP tricks, for the most part, we can turn each word in a document (or perhaps each bigram or n-gram in a document) into a feature. The Naive part of the Naive Bayesian Classifier stems from the naive assumption that all features in one's analysis are considered to be independent. If  and  are known to be independent, then . In other words, you just multiply the probabilities together. Shh, don't tell anyone, but this assumption is actually wrong! Certainly, if a document contains the word algorithm, it's more likely to contain the word probability than some randomly selected document. Thus,  Pr(\text{algorithm}) \cdot Pr(\text{probability})" />, violating the assumption. Despite this "flaw", the Naive Bayesian Classifier works remarkably will on many problems. If one employs the common approach of converting a document into bigrams (pairs of words instead of single words), then you can capture a good deal of this correlation indirectly. In the final leg of the discussion, we explore the question of whether or not a Naive Bayesian Classifier would be a good choice for detecting fake news.    

Embark on a journey to master distributed deep learning with the "Apache Spark Deep Learning Cookbook". Designed specifically for leveraging the capabilities of Apache Spark, TensorFlow, and Keras, this book offers over 80 problem-solving recipes to efficiently train and deploy state-of-the-art neural networks, addressing real-world AI challenges. What this Book will help me do Set up and configure a working Apache Spark environment optimized for deep learning tasks. Implement distributed training practices for deep learning models using TensorFlow and Keras. Develop and test neural networks such as CNNs and RNNs targeting specific big data problems. Apply Spark's built-in libraries and integrations for enhanced NLP and computer vision applications. Effectively manage and preprocess large datasets using Spark DataFrames for machine learning tasks. Author(s) Authors Ahmed Sherif and None Ravindra bring years of experience in deep learning, Apache Spark use cases, and hands-on practical training. Their collective expertise has contributed to designing this cookbook approach, focusing on clarity and usability for readers tackling challenging machine learning scenarios. Who is it for? This book is ideal for IT professionals, data scientists, and software developers with foundational understanding of machine learning concepts and Apache Spark framework capabilities. If you aim to scale deep learning and integrate efficient computing with Spark's power, this guide is for you. Familiarity with Python will help maximize the book's potential.

AI and Machine Learning will become an integral part of your marketing analytics life so before Matt Gershoff explains how it works, Jim walks you through what it is and how it is being used. From natural language processing and computer vision to chatbots and robots, you'll see how AI is applied to customer interaction. Then, Jim dives into machine learning so you can determine which software services are worth your time, communicate better with the data scientists in your company, decide to become one yourself, and figure out how and where to bring AI and ML into your marketing tool suite.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

Jeff Palmucci / @TripAdvisor talk about building a Machine Learning Team and shared some best practices for running a data-driven startup

Timeline: 0:29 Jeff's journey. 8:28 Jeff's experience of working in different eras of data science. 10:34 Challenges in working on a futuristic startup. 13:40 Entrepreneurship and ML solutions. 16:42 Putting together a ML team. 20:32 How to chose the right use case to work on? 22:20 Hacks for putting together a group for ML solutions. 24:40 Convincing the leadership of changing the culture. 29:00 Thought process of putting together an ML group. 31:36 How to gauge the right data science candidate? 35:46 Important KPIs to consider while putting together a ML group. 38:30 The merit of shadow groups within a business unit. 41:05 Jeff's key to success. 42:58 How is having a hobby help a data science leader? 45:05 Appifying is good or bad? 52:07 The fear of what ML throws out. 54:09 Jeff's favorite reads. 55:34 Closing remarks.

Podcast Link: https://futureofdata.org/jeff-palmucci-tripadvisor-discusses-managing-machinelearning-ai-team/

About Jeff Palmucci: As a serial entrepreneur, Jeff has started several companies. He was VP of Software Development for Optimax Systems, a developer of scheduling systems for manufacturing operations acquired by i2 Technologies. As a Founder and CTO of programmatic hedge fund Percipio Capital Management, he helped lead the company to an acquisition by Link Ventures. Jeff is currently leading the Machine Learning group at Tripadvisor, which does various machine learning projects across the company, including natural language processing, review fraud detection, personalization, information retrieval, and machine vision. Jeff has publications in natural language processing, machine learning, genetic algorithms, expert systems, and programming languages. When Jeff is not writing code, he enjoys going to innumerable rock concerts as a professional photographer.

Jeff's Favorite Authors (Genre: Science Fiction): Vernor Vinge http://amzn.to/2ygDPOu Stephen Baxter http://amzn.to/2ygG6cn

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy

A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist—but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company's marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms—where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the "need-to-know" aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way. Simple illustrations clarify complex concepts, and case studies show how real-world companies are taking the next leap forward. Straightforward, pragmatic, and with no math required, this book will help you: Speak intelligently about Artificial Intelligence and its advantages in marketing Understand how marketers without a Data Science degree can make use of machine learning technology Collaborate with data scientists as a subject matter expert to help develop focused-use applications Help your company gain a competitive advantage by leveraging leading-edge technology in marketing Marketing and data science are two fast-moving, turbulent spheres that often intersect; that intersection is where marketing professionals pick up the tools and methods to move their company forward. Artificial Intelligence and Machine Learning provide a data-driven basis for more robust and intensely-targeted marketing strategies—and companies that effectively utilize these latest tools will reap the benefit in the marketplace. Artificial Intelligence for Marketing provides a nontechnical crash course to help you stay ahead of the curve.

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

• ERRATA (As Reported by Peter: "The book Peter mentioned (at 46:20) by Stuart Russell, "Do the Right Thing", was published in 2003, and not recently"

In this session Peter Morgan, CEO Deep Learning Partnership sat with Vishal Kumar, CEO AnalyticsWeek and shared his thoughts around Deep Learning, Machine Learning and Artificial Intelligence. They've discussed some of the best practices when it comes to picking right solution, right vendor and what are some of the keyword means.

Here's Peter's Bio: Peter Morgan is a scientist-entrepreneur starting out in high energy physics enrolled in the PhD program at the University of Massachusetts at Amherst. After leaving UMass, and founding my own company, Peter has moved into computer networks, designing, implementing and troubleshooting global IP networks for companies such as Cisco, IBM and BT Labs. After getting an MBA and dabbling in financial trading algorithms. Peter has worked for three years on an experiment lead by Stanford University to measure the mass of the neutrino. Since 2012. He had been working in Data Science and Deep Learning, founding an AI Solutions company in Jan 2016.

As an entrepreneur Peter has founded companies in the AI, social media, and music industries. He has also served on the advisory board of technology startups. Peter is a popular speaker at conferences, meetups and webinars. He has cofounded and currently organize meetups in the deep learning space. Peter has business experience in the USA, UK and Europe.

Today, as CEO of Deep Learning Partnership, He leads the strategic direction and business development across product and services. This includes sales and marketing, lead generation, client engagement, recruitment, content creation and platform development. Deep Learning technologies used include computer vision and natural language processing and frameworks like TensorFlow, Keras and MXnet. Deep Learning Partnership design and implement AI solutions for our clients across all business domains.

Interested in sharing your thought leadership with our global listeners? Register your interest @ http://play.analyticsweek.com/guest/

Mastering Text Mining with R is your go-to guide for learning how to process and analyze textual data using R. Throughout the book, you'll gain the skills necessary to perform data extraction and natural language processing, equipping you with practical applications tailored to real-world scenarios. What this Book will help me do Learn to access and manipulate textual data from various sources using R. Understand text processing techniques and employ them with tools like OpenNLP. Explore methods for text categorization, reduction, and summarization with hands-on exercises. Perform text classification tasks such as sentiment analysis and entity recognition. Build custom applications using text mining techniques and frameworks. Author(s) Ashish Kumar is a seasoned data scientist and software developer with years of experience in text analytics and the R programming language. He has a knack for explaining complex topics in an accessible and practical manner, ideal for learners embracing their text mining journey. Who is it for? This book is for anyone keen on mastering text mining with R. If you're an R programmer, data analyst, or data scientist looking to delve into text analytics, you'll find it ideal. Some familiarity with basic programming and statistics will enhance your experience, but all concepts are introduced clearly and effectively.

In "Apache Spark for Data Science Cookbook," you'll delve into solving real-world analytical challenges using the robust Apache Spark framework. This book features hands-on recipes that cover data analysis, distributed machine learning, and real-time data processing. You'll gain practical skills to process, visualize, and extract insights from large datasets efficiently. What this Book will help me do Master using Apache Spark for processing and analyzing large-scale datasets effectively. Harness Spark's MLLib for implementing machine learning algorithms like classification and clustering. Utilize libraries such as NumPy, SciPy, and Pandas in conjunction with Spark for numerical computations. Apply techniques like Natural Language Processing and text mining using Spark-integrated tools. Perform end-to-end data science workflows, including data exploration, modeling, and visualization. Author(s) Nagamallikarjuna Inelu and None Chitturi bring their extensive experience working with data science and distributed computing frameworks like Apache Spark. Nagamallikarjuna specializes in applying machine learning algorithms to big data problems, while None has contributed to various big data system implementations. Together, they focus on providing practitioners with practical and efficient solutions. Who is it for? This book is primarily intended for novice and intermediate data scientists and analysts who are curious about using Apache Spark to tackle data science problems. Readers are expected to have some familiarity with basic data science tasks. If you want to learn practical applications of Spark in data analysis and enhance your big data analytics skills, this resource is for you.

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. Practical Data Science with Hadoop® and Spark The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining. Introduces text analysis and text mining tools Provides a comprehensive overview of costs and benefits Introduces the topic, making it accessible to a general audience in a variety of fields, including examples from biology, chemistry, sociology, and criminology

Practical Data Analysis Cookbook takes you on a comprehensive journey to mastering data exploration and analysis using Python. From data cleaning and transformation to building predictive and classification models, this book provides practical recipes for tackling real-world data challenges and extracting valuable insights. What this Book will help me do Efficiently clean, transform, and explore datasets using tools like pandas and OpenRefine. Develop predictive models for time series and other datasets using Python libraries such as scikit-learn and Statsmodels. Apply clustering and classification techniques to real-world data problems to gain actionable insights. Explore advanced topics like natural language processing and graph theory concepts using specialized tools. Build the skills to solve practical data modeling problems encountered in a data science role. Author(s) None Drabas is an experienced data scientist and author who specializes in Python-based data analysis. With a background in tackling intricate data-driven problems, None brings real-world experience to the readers. In creating this Cookbook, None adopts a step-by-step approach, making complex techniques accessible to learners of all backgrounds. Who is it for? If you are a data analyst, data scientist, or someone interested in exploring Python for practical data problems, this book is for you. It suits beginners starting their data journey and intermediate professionals looking to enhance their toolset. With clear instructions, it's ideal for anyone willing to build practical skills and tackle real-world challenges in data analysis.

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user. Discusses data simplification principles, methods, and tools that must be studied and mastered Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data Explains how to best utilize indexes to search, retrieve, and analyze textual data Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

The Example-Rich, Hands-On Guide to Data Munging with Apache Hadoop TM Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project. Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark. This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop. Coverage includes A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis Assessing tradeoffs in common approaches to imputing missing values Implementing quality checks with Pig or Hive UDFs Transforming raw data into “feature matrix” format for machine learning algorithms Choosing features and instances Implementing text features via “bag-of-words” and NLP techniques Handling time-series data via frequency- or time-domain methods Manipulating feature values to prepare for modeling Data Munging with Hadoop is part of a larger, forthcoming work entitled Data Science Using Hadoop. To be notified when the larger work is available, register your purchase of Data Munging with Hadoop at informit.com/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”