Data Without Labels: Practical Unsupervised Machine Learning
暫譯: 無標籤數據：實用的無監督機器學習
Verdhan, Vaibhav
- 出版商: Manning
- 出版日期: 2025-07-08
- 售價: $1,990
- 貴賓價: 9.5 折 $1,891
- 語言: 英文
- 頁數: 352
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617298727
- ISBN-13: 9781617298721
-
相關分類:
Machine Learning
尚未上市，無法訂購
相關主題
商品描述
Discover all-practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you'll learn:
Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You'll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don't get bogged down in theory--the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You'll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Unsupervised learning and machine learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss. About the book
Data Without Labels teaches you to apply a full spectrum of machine learning algorithms to raw data. You'll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You'll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more--and you'll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you'll find quizzes, practice datasets, and links to research papers to help you lock in what you've learned and expand your knowledge. About the reader
For developers and data scientists. Basic Python experience required. About the author
Vaibhav Verdhan is a seasoned data science professional with rich experience across geographies and domains. He has led multiple engagements in machine learning and artificial intelligence. A leading industry expert, Vaibhav is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland where he works as a principal data scientist.
- Fundamental building blocks and concepts of machine learning and unsupervised learning
- Data cleaning for structured and unstructured data like text and images
- Clustering algorithms like kmeans, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering
- Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE
- Association rule algorithms like aPriori, ECLAT, SPADE
- Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
- Building neural networks such as GANs and autoencoders
- Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling
- Association rule algorithms like aPriori, ECLAT, and SPADE
- Working with Python tools and libraries like sklearn, bumpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, andFflask
- How to interpret the results of unsupervised learning
- Choosing the right algorithm for your problem
- Deploying unsupervised learning to production
Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You'll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don't get bogged down in theory--the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You'll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Unsupervised learning and machine learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss. About the book
Data Without Labels teaches you to apply a full spectrum of machine learning algorithms to raw data. You'll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You'll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more--and you'll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you'll find quizzes, practice datasets, and links to research papers to help you lock in what you've learned and expand your knowledge. About the reader
For developers and data scientists. Basic Python experience required. About the author
Vaibhav Verdhan is a seasoned data science professional with rich experience across geographies and domains. He has led multiple engagements in machine learning and artificial intelligence. A leading industry expert, Vaibhav is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland where he works as a principal data scientist.
商品描述(中文翻譯)
探索處理未標記數據的關鍵算法和模型的所有實用實現。書中充滿案例研究，展示如何將每種技術應用於現實世界的問題。
在無標籤數據中，您將學到：- 機器學習和無監督學習的基本構建塊和概念
- 結構化和非結構化數據（如文本和圖像）的數據清理
- 聚類算法，如kmeans、層次聚類、DBSCAN、高斯混合模型和光譜聚類
- 降維方法，如主成分分析（PCA）、SVD、多維縮放和t-SNE
- 關聯規則算法，如aPriori、ECLAT、SPADE
- 無監督時間序列聚類、高斯混合模型和統計方法
- 構建神經網絡，如GANs和自編碼器
- 降維方法，如主成分分析和多維縮放
- 關聯規則算法，如aPriori、ECLAT和SPADE
- 使用Python工具和庫，如sklearn、numpy、Pandas、matplotlib、Seaborn、Keras、TensorFlow和Flask
- 如何解釋無監督學習的結果
- 為您的問題選擇合適的算法
- 將無監督學習部署到生產環境
無標籤數據介紹了數學技術、關鍵算法和Python實現，幫助您為未標註數據構建機器學習模型。您將發現無需人工干預的無監督機器學習方法，這些方法仍然能夠解開原始的現實世界數據集，並支持您業務的明智戰略決策。 不要陷入理論的泥潭——本書彌合了複雜數學與實用Python實現之間的鴻溝，涵蓋了從模型開發到生產部署的全過程。您將發現機器學習和無監督學習的商業用例，並獲取有見地的研究論文以擴展您的知識。 購買印刷版書籍可獲得Manning Publications提供的免費PDF、Kindle和ePub格式電子書。 關於技術
無監督學習和機器學習算法從未標註的數據集中推斷信息。自組織的機器學習方法非常適合發現人類可能錯過的模式。 關於本書
無標籤數據教您將全範圍的機器學習算法應用於原始數據。您將掌握從kmeans和層次聚類到像GANs和限制玻爾茲曼機這樣的高級神經網絡的所有內容。您將學習不同模型的商業用例，並掌握結構化、文本和圖像數據的最佳實踐。每種新算法都會通過零售、航空、銀行等案例研究進行介紹，您將開發Python解決方案來解決這些現實世界的問題。在每章結尾，您將找到測驗、練習數據集和研究論文的鏈接，以幫助您鞏固所學並擴展知識。 關於讀者
針對開發人員和數據科學家。需要基本的Python經驗。 關於作者
Vaibhav Verdhan是一位經驗豐富的數據科學專業人士，擁有跨地區和領域的豐富經驗。他曾領導多個機器學習和人工智能的項目。作為行業領先的專家，Vaibhav經常在會議和聚會上發言，並指導學生和專業人士。目前，他居住在愛爾蘭，擔任首席數據科學家。
作者簡介
Vaibhav Verdhan is a seasoned data science professional with rich experience across geographies and domains. He has led multiple engagements in machine learning and artificial intelligence. A leading industry expert, Vaibhav is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland where he works as a principal data scientist.
作者簡介(中文翻譯)
Vaibhav Verdhan 是一位經驗豐富的資料科學專業人士，擁有跨地區和領域的豐富經驗。他曾主導多個機器學習和人工智慧的專案。作為業界的領先專家，Vaibhav 經常在會議和聚會上發表演講，並指導學生和專業人士。目前，他居住在愛爾蘭，擔任首席資料科學家。
