Python machine learning

Come along with Bear and learn about the different rooms of his house. Rhyme and repetition builds vocabulary and a full spread "blueprint" of Bear's house reinforces the learning layers.Winner of the

Pizzoli’s Good Night Owl is a modern twist on the classic tale of not wanting to go to bed. Teaching the importance of learning to accept things beyond one’s control, YOTTOY’s Owl Soft Toy stays true

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

Python機器學習第三版（下） Python Machine Learning - Third Edition 第三版－最新修訂版，新增TensorFlow 2、GAN和強化學習等實用內容 使用Python的scikit-learn和TensorFlow 2融會貫通機器學習與深度學習 循序漸進、由淺入深，好評熱銷再進化！最新修訂的《Python機器學習第三版》是一本不容錯過的全方位指南，也是讀者

《Python機器學習》面向機器學習新手，主要內容如下： ● Python機器學習的一些基本庫，包括NumPy、Pandas和matplotlib庫 ● 常見的機器學習算法，包括回歸、聚類、分類和異常檢測 ● 使用Python和Scikit-learn庫進行機器學習 ● 將機器學習模型部署為Web服務 ● 使用Microsoft Azure Machine Learning Studio進行機器學

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

Learning Chinese Language and Culture is an intermediate-level textbook intended to be used throughout the entire school year and designed mainly for students who have completed introductory courses o

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook f

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il

An introduction to machine learning that includes the fundamental techniques, methods, and applications Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concept

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are d

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, inc

Machine learning is an exciting new way to use computers to perform tasks that require the ability to learn from experience. In order to make machine learning a reality, programmers rely on special la

In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This BookFully-coded working examples using a wide range of machine learning libraries and

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business envir

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for a

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the mo

The book adopts a tutorial-based approach to introduce the user to Scikit-learn. If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in r

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology a

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines.

Meet Ursula, the villain from Disney’sThe Little Mermaid,in this Step 3-leveled reader in the Step Into Reading program.Follow the sea witch Ursula’s story from Disney’sThe Little Mermaidin this Step 3 Step into Reading leveled reader! Meet her sidekicks, learn about her magical powers, and more! This book is perfect for young children learning to read independently!Step 3 readersfeature engaging characters in easy-to-follow plots about popular topics. They are for children who are ready to read on their own.

Pete the Cat explores his family tree in this I Can Read Phonics Level 2 story full of long vowel sounds and consonant digraphs perfect for learning to read. By the #1 New York Times bestselling team Kimberly and James Dean!Pete the Cat is making a family tree. He adds Mom, Dad, and Bob. Who is next?Meet the grooviest members of Pete the Cat's super cool family in this highly decodable Level 2 I Can Read Phonics book designed in conjunction with a phonics expert to help kids learn to read. Includes frontmatter with tips on how to support new readers with each sound in the story.For more I Can Read Phonics, check out:Little Critter: Can I Do It? (I Can Read Phonics Level 1)

A box set of Books #8-14 of the bestselling Baby-sitters Club graphic novels!Join Kristy, Mary Anne, Claudia, Stacey, Dawn, Mallory, and Jessi on their baby-sitting adventures! No matter what comes up -- rowdy kids, snobby neighbors, spooky letters, learning a new language -- you can count on the BSC.Bestselling cartoonists Gale Galligan (Book #8), Gabriela Epstein (Books #9 and #11), Chan Chau (Books #10 and #12), Cynthia Yuan Cheng (Book #13), and Ellen T. Crenshaw (Book #14) perfectly capture all the drama and humor of the original novels by Ann M. Martin. This box set is perfect for readers who are new to the series and longtime fans!

只要看看資料，就能預測未來？時間序列分析，讓數據告訴你答案。銷售額為什麼忽高忽低？廣告真的有效，還是只是運氣好？預算該怎麼分配，才能帶來最大的成效？這些問題，其實都藏在「隨時間變化的資料」之中★東大專家親授！第一本專為「商務實戰」打造的時序分析漫畫教程。★拒絕死背公式！從 ARIMA 模型到 mROI 評估，看漫畫就能學會預測未來的技術。從基礎觀念到 ARIMA、預測建模一次學會銷售分析、廣告成效評估與預算最佳化Python 實作 × 商務情境 × 圖像化解說「數據真的能預測未來嗎？」家電大廠資料分析師帶你從零開始，把冰冷數據變成獲利決策！銷售額增減、庫存變動、廣告成效……我們的身邊充滿了「隨時間變化」的資料，但你懂得如何利用它們嗎？ 時間序列分析，常被認為是困難又抽象的統計領域，但其實，它正是解決現代商務問題的關鍵工具本書透過有趣的家電大廠漫畫故事，帶領讀者深入淺出地掌握「時間序列分析」，從最基礎的圖表化觀察，到進階的 ARIMA 模型建立，甚至連業界最關心的「廣告延續效應」與「預算分配最佳化」都一次講清楚！【本書特色】•掌握趨勢： 拆解數據中的季節性與趨勢，不再被短期波動誤導。•精準預測： 運用過往資料建模，科學化預測未來銷售走向。•廣告優化： 分析不同媒介的廣告貢獻度，找出最佳投放時機與預算比例。•完整實務流程：從資料觀察 → 建模 → 驗證 → 預測 → 最佳化，讓理論直接轉化為工作生產力。•視覺化圖解： 複雜的迴歸與建模過程轉化為圖表，用圖表與插圖理解趨勢、季節性與模型概念，一眼掌握性質與重點。•商務導向： 內容涵蓋廣告成效驗證、因果推論與預算最佳化，聚焦銷售，極具商務價值。•學習檢測： 每一章結尾設有重點延伸說明，且附完整 Notebook 與資料檔，可直接動手實作，引導你從初學者走向中級。•名師加持： 專家級作者親力親為，細緻講解如何深入挖掘數據價值。數據分析真的能改善現況嗎？這是一本為「想解決問題的人」寫的書！很多人看到「時間序列分析」就聯想到複雜的微積分與統計術語，因而望而卻步。《圖解時間序列分析》是一本專為不擅長數學、卻想把資料分析用在實務中的讀者所寫的入門到應用書籍。透過漫畫故事與大量圖解，帶你一步步理解時間序列分析的核心概念，並實際應用在銷售分析、廣告成效評估、未來預測與廣告預算最佳化等真實商務場景本書透過圖解＋漫畫＋Python 實作的方式

"《生成式AI × 穩健提示爬蟲技術 I》數據抓取篇★從Colab、Anaconda 到 Jupyter Kernel，一步步把 Python 環境建好、跑起來。★用Pandas × PyArrow × Polars，把資料整理做得更快、更穩，也更貼近實務。★透過提示工程和情境工程，讓 ChatGPT、Claude 成為你的程式開發夥伴。★結合Perplexity Comet＋爬蟲＋寄信＋行事曆，把資訊蒐集到自動化流程一次串起。★多模態OCR × Gemini和Groq API，把圖片文字直接變成可分析的結構化資料。你學過Python，卻常卡在「環境裝不好、資料處理很慢、爬蟲一下就被擋、AI 工具只會聊天」嗎？本書以「做得出成果」為主軸，帶你從 Google Colab、Anaconda、Jupyter Notebook 與 Kernel 建置開始，快速建立穩定、可重現的開發環境。接著用 Pandas 打底，涵蓋多欄多列取值、資料框合併、字串提取與條件篩選，並延伸到 PyArrow 與 Polars，讓你面對較大的資料量也能跑得快、跑得順。更關鍵的是，本書把提示工程與情境工程結合「直覺式開發」：教你如何用 ChatGPT和Claude 協作寫程式、除錯；再搭配 Perplexity Comet 進行摘要與探勘，串接 Gmail、Google 行事曆、SMTP、Resend 寄信，打造可實際應用的自動化資訊流程。最後以多模態 API（Gemini、Groq）＋中文 OCR（PaddleOCR、EasyOCR）＋爬蟲實戰進行總結，範例涵蓋電商、股價、ESG、重大訊息、交易指標等案例，並附上Gemini AI 教育版證照準備重點，適合資料分析初學者、稽核、採購、研究人員、跨域工作者，以及想把 AI 變成生產力的人。專業推薦崑山科技大學 電機工程系系主任─林俊良 博士住宅消保會創會理事長─吳翃毅博士錸成科技有限公司執行長、嘉南藥理大學兼任助理教授─凃春愛 博士伽碩職訓中心執行長─郭明洽昱創企管顧問有限公司總經理─裴有恆元大銀行系統開發部專業副理─陳尚瑋極策網路行銷有限公司總經理、崑山科技大學房地產開發與管理系講師─蔡宗甫"

The Mubbles: The Good, the Bad and the Perky is the third in the hilarious full colour comic book series by Liz Pichon, the globally bestselling creator of Tom Gates."I laughed so much, I could barely breathe!" - Ellie"I read this book 5 times over, it was so good!" - HugoReturn to The Isle of Smile and the colourful, fun-loving Mubbles - Pop, Dash, Cheery and the gang. The Mubbles love their island, especially the mysterious Uncertain Curtain, which always delivers an unexpected surprise! What will it be today?Today the Uncertain Curtain delivers a karaoke machine - which the Mubbles love for singing their cheerful songs! This makes the Doomongers on the Isle of Gloom grumpy and desperate to stop the karaoke fun.Will sending the Clouds of Doom, the Whiff of Woe or the Beastly Bugs to the Isle of Smile keep the merry Mubbles quiet once and for all?Filled with colourful characters, boundless fun and lots of adventures this series is a guaranteed hit for kids. Featuring a QR code that wi