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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 Unsupervised Learning with Generative AI you'll learn:
Unsupervised Learning with Generative AI 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
Unsupervised Learning with Generative AI 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
Unsupervised Learning with Generative AI 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
Unsupervised Learning with Generative AI 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)
發現處理未標記數據的關鍵算法和模型的所有實際應用。充滿了案例研究，展示如何將每種技術應用於實際問題。
在使用生成式人工智能進行非監督學習中，您將學到:- 機器學習和非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、高斯混合模型和譜聚類
- 降維方法，如主成分分析（PCA）、奇異值分解（SVD）、多維尺度和t-SNE
- 關聯規則算法，如aPriori、ECLAT和SPADE
- 非監督時間序列聚類、高斯混合模型和統計方法
- 構建生成對抗網絡（GANs）和自編碼器等神經網絡
- 降維方法，如主成分分析和多維尺度
- 關聯規則算法，如aPriori、ECLAT和SPADE
- 使用Python工具和庫，如sklearn、bumpy、Pandas、matplotlib、Seaborn、Keras、TensorFlow和Flask
- 如何解釋非監督學習的結果
- 為您的問題選擇合適的算法
- 將非監督學習部署到生產環境
使用生成式人工智能進行非監督學習介紹了數學技術、關鍵算法和Python實現，幫助您構建用於未標記數據的機器學習模型。您將發現無需監督的機器學習方法，可以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。 不要陷入理論中-本書填補了復雜數學和實際Python實現之間的差距，涵蓋了從端到端的模型開發，一直到生產部署。您將了解機器學習和非監督學習的業務用例，並獲取有見地的研究論文以完善您的知識。 購買印刷版書籍將包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。 關於技術
非監督學習和機器學習算法從未標記的數據集中推斷。自組織的機器學習方法非常適合發現人類可能忽略的模式。 關於本書
使用生成式人工智能進行非監督學習教您如何將全譜的機器學習算法應用於原始數據。您將掌握從kmeans和層次聚類到高級神經網絡（如GANs和受限玻爾茨曼機）的所有內容。您將學習不同模型的業務用例，並掌握結構化、文本和圖像數據的最佳實踐。每個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問題。在每章的末尾，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您的知識。 關於讀者
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，擁有豐富的地理和領域經驗。他在機器學習和人工智能方面領導了多個項目。作為一位領先的行業專家，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，擔任首席數據科學家的職位。
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。他在機器學習和人工智慧方面領導了多個項目。作為一位領先的行業專家，Vaibhav 經常在會議和聚會上演講，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的職位。
