Thoughtful Machine Learning With Python
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Thoughtful Machine Learning With Python
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Author : Matthew Kirk
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-01-16
Thoughtful Machine Learning With Python written by Matthew Kirk 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 2017-01-16 with Computers categories.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
Thoughtful Machine Learning With Python
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Author : Matthew Kirk
language : en
Publisher:
Release Date : 2017
Thoughtful Machine Learning With Python written by Matthew Kirk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
Thoughtful Machine Learning
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Author : Matthew Kirk
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-09-26
Thoughtful Machine Learning written by Matthew Kirk 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 2014-09-26 with Computers categories.
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
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Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-30
Python Machine Learning By Example written by Yuxi (Hayden) Liu 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 2020-10-30 with Computers categories.
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook Description Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems. What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is for If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
Introduction To Machine Learning With Python
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Author : Andreas C. Müller
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-09-26
Introduction To Machine Learning With Python written by Andreas C. Müller 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 2016-09-26 with Computers categories.
Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
Think Ai
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Author : Swapnali Joshi Naik
language : en
Publisher: BPB Publications
Release Date : 2022-06-28
Think Ai written by Swapnali Joshi Naik and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Computers categories.
Develop AI based real-world Applications KEY FEATURES ● Provides a practical understanding of AI, including its concepts, tools and techniques. ● Includes step-by-by-step instructions for implementing machine learning and deep learning algorithms and features. ● Complex datasets and examples are used to expose mathematical illustrative and pseudo-coded examples. DESCRIPTION "Think AI" is a rapid-learning book that covers a wide range of Artificial Intelligence topics, including Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing. Most popular Python libraries and toolkits are applied to develop intelligent and thoughtful applications. With a solid grasp of python programming and mathematics, you may use this book's statistical models and AI algorithms to meet AI needs and data insight issues. Each chapter in this book guides you swiftly through the core concepts and then directly to their implementation using Python toolkits. This book covers the techniques and skill sets required for data collection, pre-processing, installing libraries, preparing data models, training and deploying the models, and optimising model performance. The book guides you through the OpenCV toolkit for real-time picture recognition and detection, allowing you to work with computer vision. The book describes how to analyse linguistic data and conduct text mining using the NLTK toolbox and provides a brief overview of NLP ideas. Throughout the book, you will utilise major Python libraries and toolkits such as pandas, TensorFlow, scikit-learn, and matplotlib. WHAT YOU WILL LEARN ● Work with Jupyter and various Python libraries, including scikit-learn, NLTK, and TF. ● Build and implement ML models and neural networks using TensorFlow and Keras. ● Utilize OpenCV for real-time image processing, face detection, and face recognition. ● Know how to interact and process textual data using NLTK toolkit. ● Deep dive on Exploratory Data Analysis (EDA) with pandas, matplotlib and seaborn. WHO THIS BOOK IS FOR Whether you're a student, newbie or an existing AI developer, this book will help you get up to speed with various domains of AI, including ML, Deep Learning and NLP. Knowing the basics of python and understanding mathematics will be beneficial. TABLE OF CONTENTS 1. Introducing Artificial Intelligence 2. Essentials of Python and Data Analysis 3. Data Preparation and Machine Learning 4. Computer Vision using OpenCV 5. Fundamentals of Neural Networks and Deep Learning 6. Natural Language Processing
Thoughtful Machine Learning
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Author : Matthew Kirk
language : en
Publisher: Oreilly & Associates Incorporated
Release Date : 2014-10-12
Thoughtful Machine Learning written by Matthew Kirk and has been published by Oreilly & Associates Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-12 with Computers categories.
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Supervised Machine Learning With Python
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Author : Taylor Smith
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-27
Supervised Machine Learning With Python written by Taylor Smith 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 2019-05-27 with Computers categories.
Teach your machine to think for itself! Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.
Python Machine Learning Projects
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Author : Lisa Tagliaferri
language : en
Publisher: DigitalOcean
Release Date : 2019-05-02
Python Machine Learning Projects written by Lisa Tagliaferri and has been published by DigitalOcean this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-02 with Computers categories.
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.