商品描述
An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations Key FeaturesA balanced combination of underlying mathematical theories & practical examples with Python codeCoverage of latest topics like multi-label classification, Text Mining, Doc2Vec, Word2Vec, XMeans clustering, unsupervised outlier detection, techniques to deploy ML models in production-grade systems with PMML, etc Coverage of sufficient & relevant visualization techniques specific to any topic DescriptionThis book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on scikit-learn, but other Python libraries like Gensim or PyTorch will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models. What will you learnGet familiar with practical concepts of Machine Learning from ground zero Learn how to deploy Machine Learning models in production Understand how to do Data Science Storytelling Explore the latest topics in the current industry about Machine LearningWho this book is forThis book would be ideal for experienced Software Professionals who are trying to get into the field of Machine Learning. Anyone who wishes to Learn Machine Learning concepts and models in the production lifecycle.Table of Contents1. Introduction to Machine Learning & Mathematical preliminaries2. Classification3. Regression4. Clustering5. Deep Learning & Neural Networks6. Miscellaneous Unsupervised Learning7. Text Mining8. Machine Learning models in production9. Case Studies & Data Science StorytellingAbout the Author Avishek has a Master's degree in Data Analytics & Machine Learning from BITS (Pilani) and a Bachelor's degree in Computer Science from West Bengal University of Technology (WBUT). He has more than 14 years of experience in different renowned companies like VMware, Cognizant, Cisco, Mobile Iron, etc. He started his career as a Java developer and later moved to the core area of Machine Learning around five years back. He has practical experience in the design & development of Machine Learning systems, starting from inception to production in multiple organizations. Strong foundations in Mathematics/Statistics and a solid experience in product development had helped him to excel quickly in the world of ML & Data Science. He has shared his knowledge & experience through this book, which can help any Software Engineer to kick start in this area. He also writes blogs, and the same can be found at https: //medium.com/@avisheknag17Your Blog links: https: //medium.com/@avisheknag17Your LinkedIn Profile: https: //www.linkedin.com/in/avishek-nag-957a00
商品描述(中文翻譯)
一本易於理解的實用機器學習技術指南,具備數學基礎
主要特點
- 將基礎數學理論與實用範例及 Python 代碼平衡結合
- 涵蓋最新主題,如多標籤分類、文本挖掘、Doc2Vec、Word2Vec、XMeans 聚類、無監督異常檢測、在生產級系統中部署機器學習模型的技術(使用 PMML 等)
- 涵蓋與任何主題相關的足夠且相關的可視化技術
描述
本書非常適合希望從零開始學習機器學習的在職專業人士。第一章將是介紹性章節,讓讀者對機器學習的概念及所需的數學理論感到舒適。將會有基礎數學理論與任何機器學習主題及其使用 Python 實現的平衡結合。大多數實現將基於 scikit-learn,但其他 Python 庫如 Gensim 或 PyTorch 也將用於一些主題,如文本分析或深度學習。本書將根據主要機器學習主題(如分類、回歸、聚類、深度學習、文本挖掘等)劃分章節。本書還將解釋將機器學習模型投入生產級系統的不同技術,使用大數據或非大數據的標準來導出模型。
您將學到什麼
- 從零開始熟悉機器學習的實用概念
- 學習如何在生產環境中部署機器學習模型
- 理解如何進行數據科學故事講述
- 探索當前行業中有關機器學習的最新主題
本書適合對象
本書非常適合有經驗的軟體專業人士,想要進入機器學習領域的任何人。任何希望學習機器學習概念和模型在生產生命周期中的人。
目錄
1. 機器學習與數學基礎介紹
2. 分類
3. 回歸
4. 聚類
5. 深度學習與神經網絡
6. 雜項無監督學習
7. 文本挖掘
8. 生產中的機器學習模型
9. 案例研究與數據科學故事講述
關於作者
Avishek 擁有 BITS(比拉尼)的數據分析與機器學習碩士學位,以及西孟加拉技術大學(WBUT)的計算機科學學士學位。他在 VMware、Cognizant、Cisco、Mobile Iron 等多家知名公司擁有超過 14 年的經驗。他的職業生涯始於 Java 開發,約五年前轉向機器學習的核心領域。他在多個組織中擁有從構思到生產的機器學習系統設計與開發的實踐經驗。扎實的數學/統計基礎和豐富的產品開發經驗使他能夠迅速在機器學習與數據科學的世界中脫穎而出。他通過本書分享了他的知識與經驗,幫助任何軟體工程師在這個領域起步。他還撰寫博客,相關內容可在 https://medium.com/@avisheknag17 找到。
您的博客連結: https://medium.com/@avisheknag17
您的 LinkedIn 個人檔案: https://www.linkedin.com/in/avishek-nag-957a00