About This Book
Leverage Python' s most powerful open-source libraries for deep learning, data wrangling, and data visualization
Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
Explore how to use different machine learning models to ask different questions of your data
Learn how to build neural networks using Keras and Theano
Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
Discover how to embed your machine learning model in a web application for increased accessibility
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Organize data using effective pre-processing techniques
Get to grips with sentiment analysis to delve deeper into textual and social media data
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
發表於2025-01-28
Python Machine Learning 2025 pdf epub mobi 電子書 下載
充其量不過是幾個常用python ML包(scikit NumPy SciPy matplotlib pandas)的 cookbook 罷瞭。 基本上每節的流程就是先告訴你一個ML概念大概是怎麼迴事,真的很大概,不過好處是至少會告訴你為什麼要這麼做。然後用一段示例代碼告訴你這個東西在Python ML包裏要調用哪幾個接口...
評分充其量不過是幾個常用python ML包(scikit NumPy SciPy matplotlib pandas)的 cookbook 罷瞭。 基本上每節的流程就是先告訴你一個ML概念大概是怎麼迴事,真的很大概,不過好處是至少會告訴你為什麼要這麼做。然後用一段示例代碼告訴你這個東西在Python ML包裏要調用哪幾個接口...
評分中文翻譯(非官方) https://www.gitbook.com/book/ljalphabeta/python-/details ==========================================================================================================================================================
評分充其量不過是幾個常用python ML包(scikit NumPy SciPy matplotlib pandas)的 cookbook 罷瞭。 基本上每節的流程就是先告訴你一個ML概念大概是怎麼迴事,真的很大概,不過好處是至少會告訴你為什麼要這麼做。然後用一段示例代碼告訴你這個東西在Python ML包裏要調用哪幾個接口...
評分充其量不過是幾個常用python ML包(scikit NumPy SciPy matplotlib pandas)的 cookbook 罷瞭。 基本上每節的流程就是先告訴你一個ML概念大概是怎麼迴事,真的很大概,不過好處是至少會告訴你為什麼要這麼做。然後用一段示例代碼告訴你這個東西在Python ML包裏要調用哪幾個接口...
圖書標籤: 機器學習 Python MachineLearning 計算機 python 數據分析 ML 數據挖掘
說實話這書沒有想象中的好,它的定位是cookbook,對於ML的原理是有些闡述的,但是講的不深,好多地方就是列齣一個公式,讓我這種數學渣看起來比較費勁,需要不斷的查各種資料,對於400多頁的書,也就能寫到這種程度瞭。 還有本書的typo是比較多的。 本書的例子還好,ML的各方麵都有涉及,對於入門是閤適的。 看完本書我覺得應該讀一些理論方麵的書,然後可以再速刷一遍,鍛煉動手能力。 最後兩章還沒讀完,Deep Learning不好懂啊!
評分錯誤很多,直接上GitHub上找到勘誤和代碼,改正後很舒暢,非常入門和實用
評分嘴上說著不要還是勉強翻完瞭。很失望,大段代碼和前後不搭的實例缺少完善的理論框架而且不係統,編寫太隨意難得要領。不過還是姑且有些有用內容,不算太虧。
評分這本書類似於tutorial,對理論隻有一點點涉及,但是python code,還有如何實現是非常詳細的。非常好。
評分嘴上說著不要還是勉強翻完瞭。很失望,大段代碼和前後不搭的實例缺少完善的理論框架而且不係統,編寫太隨意難得要領。不過還是姑且有些有用內容,不算太虧。
Python Machine Learning 2025 pdf epub mobi 電子書 下載