machine learning in python

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Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using PythonUnsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis (English Edition)Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python【電子書籍】 Sebastian RaschkaStatistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy)はじめてのディープラーニング -Pythonで学ぶニューラルネットワークとバックプロパゲーション- (Machine Learning)データサイエンスと機械学習 理論からPythonによる実装まで / 原タイトル:Data Science and Machine Learning 本/雑誌 (DIGITAL) / D.P.Kroese/著 Z.I.Botev/著 T.Taimre/著 R.Vaisman/著 金森敬文/監訳Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd EditionIntroduction to Machine Learning with Python A Guide for Data Scientists【電子書籍】 Sarah GuidoHands-On Machine Learning for Algorithmic Trading Design and implement investment strategies based on smart algorithms that learn from data using Python【電子書籍】 Stefan JansenFundamentals of Supervised Machine Learning With Applications in Python, R, and Stata【電子書籍】 Giovanni CerulliInterpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples【電子書籍】 Serg Mas sProbabilistic Machine Learning for Finance and Investing A Primer to Generative AI with Python【電子書籍】 Deepak K. KanungoHands-On Unsupervised Learning Using Python How to Build Applied Machine Learning Solutions from Unlabeled Data【電子書籍】 Ankur A. PatelMachine Learning in Python: Hands on Machine Learning with Python Tools, Concepts and Techniques (English Edition)Learn Python Programming for Beginners The Best Step-by-Step Guide for Coding with Python, Great for Kids and Adults. Includes Practical Exercises on Data Analysis, Machine Learning and More.【電子書籍】 Flynn FisherPythonではじめる機械学習 scikit‐learnで学ぶ特徴量エンジニアリングと機械学習の基礎 / 原タイトル:Introduction to Machine Learning with Python 本/雑誌 / AndreasC.Muller/著 SarahGuido/著 中田秀基/訳Feature Engineering for Machine Learning in Python: A Practical Guide to Creating Data Features That Drive Model Performance Using Python and Scikit-Learn: Techniques, Pipelines, and Case StudiesThe Best Python Programming Step-By-Step Beginners Guide Easily Master Software engineering with Machine Learning, Data Structures, Syntax, Django Object-Oriented Programming, and AI application【電子書籍】 Chris WilliamsonMachine Learning in Biotechnology and Life Sciences Build machine learning models using Python and deploy them on the cloud【電子書籍】 Saleh AlkhalifaLearning Genetic Algorithms with Python: Empower the Performance of Machine Learning and Artificial Intelligence Models with the Capabilities of a Powerful Search Algorithm (English Edition)【電子書籍】 Ivan GridinPython Machine Learning: Introduction to Machine Learning with Python【電子書籍】 Frank MillsteinMachine Learning with Python: The Definitive Guide to Mastering Machine Learning in Python and a Problem-Guide Solver to Creating Real-World Intelligent ... with different languages ) (English Edition)Machine Learning With Python: Principles and Practical Techniques (Elements in Earth System Governance)Machine Learning for Algorithmic Trading Predictive models to extract signals from market and alternative data for systematic trading strategies with Python【電子書籍】 Stefan JansenCausal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more【電子書籍】 Aleksander MolakFundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)Python機械学習プログラミング 達人データサイエンティストによる理論と実践 / 原タイトル:Python Machine Learning 原著第3版の翻訳 本/雑誌 (impress top gear) / SebastianRaschka/著 VahidMirjalili/著 クイープ/訳 福島真太朗/監訳Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python【電子書籍】 Francois VoronCausal Inference in Python: Applying Causal Inference in the Tech IndustryInterpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples【電子書籍】 Serg Mas s
 

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  • <p><b>This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format.</b></p><h2>Key Features</h2><ul><li>Learn applied machine learning with a solid foundation in theory</li><li>Clear, intuitive explanations take you deep into the theory and practice of Python machine learning</li><li>Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices</li></ul><h2>Book Description</h2>Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential ...
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  • 【30日間返品保証】商品説明に誤りがある場合は、無条件で弊社送料負担で商品到着後30日間返品を承ります。【最短翌日到着】正午12時まで(日曜日は午前9時まで)の注文は当日発送(土日祝も発送)。関東・関西・中部・中国・四国・九州地方は翌日お届け、東北地方・新潟県・北海道・沖縄県は翌々日にお届けします。【インボイス制度対応済み】当社ではインボイス制度に対応した適格請求書発行事業者番号(通称:T番号・登録番号)を印字した納品書(明細書)を商品に同梱してお送りしております。こちらをご利用いただくことで、税務申告時や確定申告時に消費税額控除を受けることが可能になります。また、適格請求書発行事業者番号の入った領収書・請求書をご注文履歴からダウンロードしていただくこともできます(宛名はご希望のものを入力していただけます)。ご満足のいく取引となるよう精一杯対応させていただきます。※下記に商品説明およびコンディション詳細、出荷予定・配送方法・お届けまでの期間について記載しています。ご確認の上ご購入ください。■商品名■はじめてのディープラーニング -Pythonで学ぶニューラルネットワークとバックプロパゲーション- (Machi...
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  • ご注文前に必ずご確認ください<商品説明><収録内容>1 データの取込み、要約、可視化2 統計的学習3 モンテカルロ法4 教師なし学習5 回帰6 正則化とカーネル法7 分類8 決定木とアンサンブル法9 深層学習付録<商品詳細>商品番号:NEOBK-2806509D. P. Kroese / Cho Z. I. Botev / Cho T. Taimre / Cho R. Vaisman / Cho Kanamori Takashi Bun / Kanyaku / Data Saiensu to Kikai Gakushu Riron Kara Python Niyoru Jisso Made / Original Title: Data Science and Machine Learning (DIGITAL)メディア:本/雑誌重量:340g発売日:2022/12JAN:9784807920297データサイエンスと機械学習 理論からPythonによる実装まで / 原タイトル:Data Science and Machine Learning[本/雑誌] (DIGITAL) / D.P.Kroese/著 Z.I.Botev/著 T.Taimre/著 R.Vaisman/著 金森敬文/監訳2022/12発売
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  • <p>Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.</p> <p>You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas M?ller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.</p> <p>With this book, you’ll learn:</p> <ul> <li>Fundamental concepts and applications of machine learning</li> <li>Advantages and shortcomings of widely used machine learning algorithms</li...
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  • <p><strong>Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras</strong></p> <h4>Key Features</h4> <ul> <li>Implement machine learning algorithms to build, train, and validate algorithmic models</li> <li>Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions</li> <li>Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics</li> </ul> <h4>Book Description</h4> <p>The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.</p> <p>This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a fra...
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  • <p>This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms.</p> <p>After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are ava...
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  • <p><b>A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format.</b></p><h2>Key Features</h2><ul><li>Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores</li><li>Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods</li><li>Analyze and extract insights from complex models from CNNs to BERT to time series models</li></ul><h2>Book Description</h2>Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interp...
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  • <p>There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.</p> <p>Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.</p> <p>Unl...
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  • <p>Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.</p> <p>Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.</p> <ul> <li>Compare the strengths an...
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  • <p><strong>The Complete Python Masterclass Made Easy, Even if You’ve Never Coded in Your Life!</strong></p> <p>If you go on Google right at this second and open any statistics with the most in-demand programming languages for the past 5 years until today you will consistently see in the top 3 a language called ‘Python’. More often than not, it is the number one programming language to learn year after year.</p> <p><em>But why would so many people look for Python experts?</em></p> <p>Two big reasons:</p> <ul> <li><strong>It’s an extremely powerful high-level programming language</strong></li> <li><strong>The coding syntax is very simplified, making it fail-proof to learn and execute</strong></li> </ul> <p>Combining those two things makes Python constantly being improved and updated. While learning the basics is something that will get you<br /> started, you will have the ability to grow your skills above and beyond because there’s alway...
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  • ご注文前に必ずご確認ください<商品説明>Pythonの機械学習用ライブラリの定番、scikit‐learnのリリースマネージャを務めるなど開発に深く関わる著者が、scikit‐learnを使った機械学習の方法を、ステップバイステップで解説します。ニューラルネットを学ぶ前に習得しておきたい機械学習の基礎をおさえるとともに、優れた機械学習システムを実装し精度の高い予測モデルを構築する上で重要となる「特徴量エンジニアリング」と「モデルの評価と改善」について多くのページを割くなど、従来の機械学習の解説書にはない特長を備えています。<収録内容>1章 はじめに2章 教師あり学習3章 教師なし学習と前処理4章 データの表現と特徴量エンジニアリング5章 モデルの評価と改良6章 アルゴリズムチェーンとパイプライン7章 テキストデータの処理8章 おわりに<商品詳細>商品番号:NEOBK-2097933AndreasC. Muller / Cho SarahGuido / Cho Nakata Hideki / Yaku / Python Dehajimeru Kikai Gakushu Scikit Learn De Manabu Tokucho Ryo Engineering to Kikai Gakushu No Kiso / Original Title: Introduction to Machine Learning with Pythonメディア:本/雑誌重量:653g発売日:...
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  • <p>Discover why you will be able to understand Python programming language in less than 6 hours if you can read an English sentence…</p> <p>If you see a code called "print", what do you think is going to happen?</p> <p>a. This line will be copied<br /> b. This line will be printed<br /> c. This line will be deleted</p> <p>If you have the level of a primary school kid, you´ll most likely answer "b)" and you are right.</p> <p>Python is known as the easiest programming language in the world.</p> <p>Even if it is so easy that kids can learn the basics, you are able to develop big and complex projects.</p> <p>Google Search and YouTube are just some examples of big products powered by Python.</p> <p>Statistics revealed that 6 out of 10 parents preferred their children to learn Python instead of French.<br /> There is a high demand for people to know programming language.<br /> Instead of being a language designed for computer nerds, you can use Python...
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  • <p>Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key Features ? Learn the applications of machine learning in biotechnology and life science sectors ? Discover exciting real-world applications of deep learning and natural language processing ? Understand the general process of deploying models to cloud platforms such as AWS and GCP Book Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, ...
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  • <p><strong>Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions</strong></p> <p><strong>DESCRIPTION</strong></p> <p>Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book 'Learning Genetic Algorithms with Python' guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.</p> <p>Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms.</p> <p><strong>KEY FEATU...
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  • <p>Machine learning is <strong>the science of getting machines and computers to act and learn on their own without being programmed explicitly</strong>. In just the past decade, this field has given us practical speech recognition, self-driving cars, greatly improved understanding of the overall human genome, effective web search and much more. Therefore, there is no wondering why machine learning is so pervasive today.In this book, you will learn more about <strong>interpreting machine learning techniques using Python</strong>. You will also gain practice as you implement the most popular machine learning techniques on some real-world examples and you will learn both about the theoretical and practical machine learning implementation using Python's machine learning libraries.At the end of the book, you will be able to cope with more complex machine learning issues solving your own problems using Python and its libraries specifically crafted for machine learning.</p> <p...
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  • <p><b>Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format.</b></p><h2>Key Features</h2><ul><li>Design, train, and evaluate machine learning algorithms that underpin automated trading strategies</li><li>Create a research and strategy development process to apply predictive modeling to trading decisions</li><li>Leverage NLP and deep learning to extract tradeable signals from market and alternative data</li></ul><h2>Book Description</h2>The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. Th...
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  • <p><b>Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook</b></p><h2>Key Features</h2><ul><li>Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more</li><li>Discover modern causal inference techniques for average and heterogenous treatment effect estimation</li><li>Explore and leverage traditional and modern causal discovery methods</li></ul><h2>Book Description</h2>Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal think...
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  • ご注文前に必ずご確認ください<商品説明>本書は、機械学習コンセプト全般をカバーし、理論的背景とPythonコーディングの実際を解説しています。初歩的な線形回帰から始め、ディープラーニング(CNN/RNN)、敵対的生成ネットワーク(GAN)、強化学習などを取り上げ、scikit‐learnやTensorFlowなどPythonライブラリの新版を使ってプログラミング。第3版では13〜16章の内容をほとんど刷新したほか、敵対的生成ネットワークと強化学習の章を新たに追加。機械学習プログラミングの本格的な理解と実践に向けて大きく飛躍できる一冊です。<収録内容>「データから学習する能力」をコンピュータに与える分類問題—単純な機械学習アルゴリズムの訓練分類問題—機械学習ライブラリscikit‐learnの活用データ前処理—よりよい訓練データセットの構築次元削減でデータを圧縮するモデルの評価とハイパーパラメータのチューニングのベストプラクティスアンサンブル学習—異なるモデルの組み合わせ機械学習の適用1—感情分析機械学習の適用2—Webアプリケーション回帰分析—連続値をとる目的変数の予測クラスタ分析—ラベルなしデータの分析多層人工ニューラルネットワークを一から実装ニューラルネ...
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  • <p>Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key Features ? Cover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injection ? Develop efficient RESTful APIs for data science with modern Python ? Build, test, and deploy high performing data science and machine learning systems with FastAPI Book Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate wit...
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  • <p><b>A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models</b></p><h2>Key Features</h2><ul><li>Learn how to extract easy-to-understand insights from any machine learning model</li><li>Become well-versed with interpretability techniques to build fairer, safer, and more reliable models</li><li>Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models</li></ul><h2>Book Description</h2>Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and chal...
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