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2024-12-26
Python Machine Learning 2024 pdf epub mobi 電子書 下載
中文翻譯(非官方) https://www.gitbook.com/book/ljalphabeta/python-/details ==========================================================================================================================================================
評分但是是有前提的: 1. 基礎的綫性代數知識需要大傢溫故知新一下; 2. 對於python中的numpy和pandas的一些基本操作需要熟悉; 3. 抽象能力,最好能把代數方程在大腦裏映射齣一個幾何圖形(最多三維); 隻要有瞭以上的前提,讀這本書還是挺靠譜的。
評分但是是有前提的: 1. 基礎的綫性代數知識需要大傢溫故知新一下; 2. 對於python中的numpy和pandas的一些基本操作需要熟悉; 3. 抽象能力,最好能把代數方程在大腦裏映射齣一個幾何圖形(最多三維); 隻要有瞭以上的前提,讀這本書還是挺靠譜的。
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...
評分但是是有前提的: 1. 基礎的綫性代數知識需要大傢溫故知新一下; 2. 對於python中的numpy和pandas的一些基本操作需要熟悉; 3. 抽象能力,最好能把代數方程在大腦裏映射齣一個幾何圖形(最多三維); 隻要有瞭以上的前提,讀這本書還是挺靠譜的。
圖書標籤: 機器學習 Python MachineLearning 計算機 python 數據分析 ML 數據挖掘
實用性上還是不錯的
評分實用性上還是不錯的
評分工程化相關章節蠻不錯的,推薦~
評分I am reading this book, and i think it will be worth the effort// Now i finished reading it. It is a great book despite some lack of math deduction, which complement the size of the book. It opens a door to python machine learning and it will never close i think.
評分還行吧,入門看看夠瞭
Python Machine Learning 2024 pdf epub mobi 電子書 下載