40 Algorithms Every Data Scientist Should Know

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40 Algorithms Every Data Scientist Should Know

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Author : Jürgen Weichenberger
language : en
Publisher: BPB Publications
Release Date : 2024-09-07

40 Algorithms Every Data Scientist Should Know written by Jürgen Weichenberger and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-07 with Computers categories.

DESCRIPTION Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application. This book offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML. The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals. KEY FEATURES ● Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques. ● Includes real-world examples and code snippets to illustrate the application of algorithms. ● Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels. WHAT YOU WILL LEARN ● Differences between supervised, unsupervised, and reinforcement learning. ● Gain expertise in data cleaning, feature engineering, and handling different data formats. ● Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines. ● Creating intelligent systems and solving real-world problems. ● Learn to approach AI and ML challenges with a structured and analytical mindset. WHO THIS BOOK IS FOR This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI. TABLE OF CONTENTS 1. Fundamentals 2. Typical Data Structures 3. 40 AI/ML Algorithms Overview 4. Basic Supervised Learning Algorithms 5. Advanced Supervised Learning Algorithms 6. Basic Unsupervised Learning Algorithms 7. Advanced Unsupervised Learning Algorithms 8. Basic Reinforcement Learning Algorithms 9. Advanced Reinforcement Learning Algorithms 10. Basic Semi-Supervised Learning Algorithms 11. Advanced Semi-Supervised Learning Algorithms 12. Natural Language Processing 13. Computer Vision 14. Large-Scale Algorithms 15. Outlook into the Future: Quantum Machine Learning

40 Algorithms Every Programmer Should Know

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Author : Imran Ahmad
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12

40 Algorithms Every Programmer Should Know written by Imran Ahmad and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Computers categories.

Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography Key Features Learn the techniques you need to know to design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore different types of algorithms and choose the right data structures for their optimal implementation Book DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time Predict the weather using supervised learning algorithms Use neural networks for object detection Create a recommendation engine that suggests relevant movies to subscribers Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP) Who this book is for This book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Python Cookbook

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Author : David Beazley
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-05-10

Python Cookbook written by David Beazley and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-10 with Computers categories.

If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, youâ??ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works. Topics include: Data Structures and Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions

15 Math Concepts Every Data Scientist Should Know

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Author : David Hoyle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-16

15 Math Concepts Every Data Scientist Should Know written by David Hoyle and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-16 with Computers categories.

Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithms Key Features Understand key data science algorithms with Python-based examples Increase the impact of your data science solutions by learning how to apply existing algorithms Take your data science solutions to the next level by learning how to create new algorithms Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers. Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you’ll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems. By the end of the book, you’ll have the confidence to apply key mathematical concepts to your data science challenges.What you will learn Master foundational concepts that underpin all data science applications Use advanced techniques to elevate your data science proficiency Apply data science concepts to solve real-world data science challenges Implement the NumPy, SciPy, and scikit-learn concepts in Python Build predictive machine learning models with mathematical concepts Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling Acquire mathematical skills tailored for time-series and network data types Who this book is for This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you’re looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists.

Data Science Algorithms In A Week

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Author : Dávid Natingga
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31

Data Science Algorithms In A Week written by Dávid Natingga and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.

Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Choosing Chinese Universities

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Author : Alice Y.C. Te
language : en
Publisher: Routledge
Release Date : 2022-10-07

Choosing Chinese Universities written by Alice Y.C. Te and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-07 with Education categories.

This book unpacks the complex dynamics of Hong Kong students’ choice in pursuing undergraduate education at the universities of Mainland China. Drawing on an empirical study based on interviews with 51 students, this book investigates how macro political/economic factors, institutional influences, parental influence, and students’ personal motivations have shaped students’ eventual choice of university. Building on Perna’s integrated model of college choice and Lee’s push-pull mobility model, this book conceptualizes that students’ border crossing from Hong Kong to Mainland China for higher education is a trans-contextualized negotiated choice under the "One Country, Two Systems" principle. The findings reveal that during the decision-making process, influencing factors have conditioned four archetypes of student choice: Pragmatists, Achievers, Averages, and Underachievers. The book closes by proposing an enhanced integrated model of college choice that encompasses both rational motives and sociological factors, and examines the theoretical significance and practical implications of the qualitative study. With its focus on student choice and experiences of studying in China, this book’s research and policy findings will interest researchers, university administrators, school principals, and teachers.

Doing Data Science

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Author : Cathy O'Neil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-10-09

Doing Data Science written by Cathy O'Neil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-09 with Computers categories.

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Tiny Python Projects

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Author : Ken Youens-Clark
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-21

Tiny Python Projects written by Ken Youens-Clark and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-21 with Computers categories.

”Tiny Python Projects is a gentle and amusing introduction to Python that will firm up key programming concepts while also making you giggle.”—Amanda Debler, Schaeffler Key Features Learn new programming concepts through 21-bitesize programs Build an insult generator, a Tic-Tac-Toe AI, a talk-like-a-pirate program, and more Discover testing techniques that will make you a better programmer Code-along with free accompanying videos on YouTube Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book The 21 fun-but-powerful activities in Tiny Python Projects teach Python fundamentals through puzzles and games. You’ll be engaged and entertained with every exercise, as you learn about text manipulation, basic algorithms, and lists and dictionaries, and other foundational programming skills. Gain confidence and experience while you create each satisfying project. Instead of going quickly through a wide range of concepts, this book concentrates on the most useful skills, like text manipulation, data structures, collections, and program logic with projects that include a password creator, a word rhymer, and a Shakespearean insult generator. Author Ken Youens-Clark also teaches you good programming practice, including writing tests for your code as you go. What You Will Learn Write command-line Python programs Manipulate Python data structures Use and control randomness Write and run tests for programs and functions Download testing suites for each project This Book Is Written For For readers familiar with the basics of Python programming. About The Author Ken Youens-Clark is a Senior Scientific Programmer at the University of Arizona. He has an MS in Biosystems Engineering and has been programming for over 20 years. Table of Contents 1 How to write and test a Python program 2 The crow’s nest: Working with strings 3 Going on a picnic: Working with lists 4 Jump the Five: Working with dictionaries 5 Howler: Working with files and STDOUT 6 Words count: Reading files and STDIN, iterating lists, formatting strings 7 Gashlycrumb: Looking items up in a dictionary 8 Apples and Bananas: Find and replace 9 Dial-a-Curse: Generating random insults from lists of words 10 Telephone: Randomly mutating strings 11 Bottles of Beer Song: Writing and testing functions 12 Ransom: Randomly capitalizing text 13 Twelve Days of Christmas: Algorithm design 14 Rhymer: Using regular expressions to create rhyming words 15 The Kentucky Friar: More regular expressions 16 The Scrambler: Randomly reordering the middles of words 17 Mad Libs: Using regular expressions 18 Gematria: Numeric encoding of text using ASCII values 19 Workout of the Day: Parsing CSV files, creating text table output 20 Password strength: Generating a secure and memorable password 21 Tic-Tac-Toe: Exploring state 22 Tic-Tac-Toe redux: An interactive version with type hints

Understanding Machine Learning

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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.