Download Full eBooks in PDF
Special Edition Data Science Interview Questions Solved In Python And Spark Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Special Edition Data Science Interview Questions Solved In Python And Spark book. This book definitely worth reading, it is an incredibly well-written.
Author : Antonio Gulli
Publisher : Createspace Independent Publishing Platform
Page : 198 pages
File Size : 52,9 Mb
Release : 2016-07-02
Category : Electronic
ISBN : 1534795715
Special Edition Data Science and Machine Learning Interview Questions Solved in Python and Spark with Deep Learning and Reinforcement Learning Bonus Questions
Author : Antonio Gulli
Publisher : Createspace Independent Publishing Platform
Page : 106 pages
File Size : 46,6 Mb
Release : 2015-11-18
Category : Electronic
ISBN : 1518678645
A collection of Machine Learning interview questions in Python and Spark
Author : Antonio Gulli
Publisher : CreateSpace
Page : 84 pages
File Size : 48,5 Mb
Release : 2015-09-22
Category : Electronic
ISBN : 1517216710
BigData and Machine Learning in Python and Spark
Author : Richard Wilson
Publisher : Unknown
Page : 248 pages
File Size : 49,9 Mb
Release : 2019-09-16
Category : Electronic
ISBN : 1693541378
★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★Data Science is present in our lives: newspapers talk about viral news, companies look for data scientists, businesses offer us personalized offers based on our customs and we grease the system by offering free personal information from our social networks, Internet searches and even from smart devices to control our daily physical activity.This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and draw conclusions from it: that is, show the data analysis as it is: a fascinating area, It requires many hours of careful work. Likewise, the Python programming language is analyzed, the most used in data Science due to the multitude of libraries that it facilitates, but is not limited to the standard, but presents current technologies that, with Python as an interface, will allow scaling the size of the data to the maximum. Therefore, our journey with the data will take us, for example, to know the Mongo DB database and the Spark processing environment.In this book, you will discover: What is a data scientist?What languages should be learned?The three musketeers of Data SciencePython introductionLanguages do you need to learn for data scienceThese are some of the topics covered in this book: Machine Learning AlgorithmsK NN - Nearest Neighbor MethodSVC - Support vector machineMathematics for Data AnalysisWorking with Threads in PythonWorking with processes in PythonThe book contains detailed examples of how to perform the different tasks in Python; and in addition, for the convenience of the reader of the included fragments, the access of the readers to a repository where they will find the code ready to be executed is facilitated. Also each chapter presents recommended readings to be able to deepen in those aspects that are more interesting. We invite you to immerse yourself in the exciting world of data Science in Python and explore the mysteries of Big Data and machine learning!Get fit, happy, and stress-free life by ordering your copy right away! also, Don't miss out on this Data Science from Scratch with Python!Just Scroll Up and Click the Buy Now Butto
Author : Vishwanathan Narayanan
Publisher : BPB Publications
Page : 125 pages
File Size : 49,9 Mb
Release : 2020-09-03
Category : Language Arts & Disciplines
ISBN : 9789389845853
Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features a- Understand the capabilities of the R programming language a- Most of the machine learning algorithms and their R implementation covered in depth a- Answers on conceptual data science concepts are also covered Description This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language. The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. What will you learn a- Get answers to the basics, intermediate and advanced questions on R programming a- Understand the transformation and filtering capabilities of R a- Know how to perform visualization using R Who this book is for This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. Table of Contents 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet About the Author Vishwanathan Narayanan has 18 years of experience in the field of information technology and data analysis. He made many enterprise-level applications with stable output and scalability. Advanced level data analysis for complex problems using both R and Python has been the key area of work for many years. Extreme programmer on Java, Python, R, and many more technologies
Author : Jonathan Rioux
Publisher : Simon and Schuster
Page : 716 pages
File Size : 53,9 Mb
Release : 2022-04-12
Category : Computers
ISBN : 9781638350668
Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the reader Written for data scientists and data engineers comfortable with Python. About the author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 Introduction PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK 2 Your first data program in PySpark 3 Submitting and scaling your first PySpark program 4 Analyzing tabular data with pyspark.sql 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE 6 Multidimensional data frames: Using PySpark with JSON data 7 Bilingual PySpark: Blending Python and SQL code 8 Extending PySpark with Python: RDD and UDFs 9 Big data is just a lot of small data: Using pandas UDFs 10 Your data under a different lens: Window functions 11 Faster PySpark: Understanding Spark’s query planning PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK 12 Setting the stage: Preparing features for machine learning 13 Robust machine learning with ML Pipelines 14 Building custom ML transformers and estimators
Author : Denny Lee,Tomasz Drabas
Publisher : Packt Publishing Ltd
Page : 321 pages
File Size : 47,7 Mb
Release : 2018-06-29
Category : Computers
ISBN : 9781788834254
Combine the power of Apache Spark and Python to build effective big data applications Key Features Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data Book Description Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. What you will learn Configure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environments Create DataFrames from JSON and a dictionary using pyspark.sql Explore regression and clustering models available in the ML module Use DataFrames to transform data used for modeling Connect to PubNub and perform aggregations on streams Who this book is for The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.
Author : Vishwanathan Narayanan
Publisher : Unknown
Page : 0 pages
File Size : 44,6 Mb
Release : 2020
Category : Databases
ISBN : OCLC:1249446511
Presenting the important concepts and various terminologies in a very simple and understandable format, this book provides answers to most asked questions in data science and machine learning interviews. --
Author : Jeffrey Aven
Publisher : Unknown
Page : 128 pages
File Size : 45,6 Mb
Release : 2018
Category : Big data
ISBN : 0134844858
Author : Alexey Grigorev
Publisher : Simon and Schuster
Page : 470 pages
File Size : 55,7 Mb
Release : 2021-11-23
Category : Computers
ISBN : 9781638351054
Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills! What's inside Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Deploy ML models to a production-ready environment About the reader Python programming skills assumed. No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow
Author : Frank Kane
Publisher : Unknown
Page : 128 pages
File Size : 51,5 Mb
Release : 2017
Category : COMPUTERS
ISBN : 1523112220
Author : Frank Kane
Publisher : Packt Publishing Ltd
Page : 420 pages
File Size : 54,9 Mb
Release : 2017-07-31
Category : Computers
ISBN : 9781787280229
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.
Author : Maverick Lin
Publisher : Unknown
Page : 120 pages
File Size : 52,5 Mb
Release : 2019-12-17
Category : Electronic
ISBN : 171068013X
Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics.
Author : Steven S. Skiena
Publisher : Springer
Page : 445 pages
File Size : 54,6 Mb
Release : 2017-07-01
Category : Computers
ISBN : 9783319554440
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
Author : Kevin Huo,Nick Singh
Publisher : Unknown
Page : 290 pages
File Size : 54,6 Mb
Release : 2021
Category : Big data
ISBN : 0578973839