Python Machine Learning Blueprints: Put your machine learning concepts to the test by developing real-world smart projects, 2/e (Paperback)

Alexander Combs, Michael Roman

  • 出版商: Packt Publishing
  • 出版日期: 2019-01-31
  • 定價: $1,760
  • 售價: 9.0$1,584
  • 語言: 英文
  • 頁數: 378
  • 裝訂: Paperback
  • ISBN: 1788994175
  • ISBN-13: 9781788994170
  • 相關分類: Python程式語言Machine Learning
  • 立即出貨 (庫存=1)

商品描述

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras

Key Features

  • Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras
  • Implement advanced concepts and popular machine learning algorithms in real-world projects
  • Build analytics, computer vision, and neural network projects

Book Description

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.

The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.

By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects.

What you will learn

  • Understand the Python data science stack and commonly used algorithms
  • Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
  • Understand NLP concepts by creating a custom news feed
  • Create applications that will recommend GitHub repositories based on ones you've starred, watched, or forked
  • Gain the skills to build a chatbot from scratch using PySpark
  • Develop a market-prediction app using stock data
  • Delve into advanced concepts such as computer vision, neural networks, and deep learning

Who this book is for

This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.

Table of Contents

  1. The Python Machine Learning Ecosystem
  2. Build an App to Find Underpriced Apartments
  3. Build an App to Find Cheap Airfares
  4. Forecast the IPO Market Using Logistic Regression
  5. Create a Custom Newsfeed
  6. Predict whether Your Content Will Go Viral
  7. Use Machine Learning to Forecast the Stock Market
  8. Classifying Images with Convolutional Neural Networks
  9. Building a Chatbot
  10. Build a Recommendation Engine
  11. What's next?

商品描述(中文翻譯)

探索一種以專案為基礎的方法來掌握機器學習概念,並將其應用於日常問題,使用像是scikit-learn、TensorFlow和Keras等函式庫。

主要特點:

- 熟悉Python的機器學習函式庫,包括scikit-learn、TensorFlow和Keras。
- 在實際專案中實現高級概念和流行的機器學習演算法。
- 建立分析、電腦視覺和神經網路專案。

書籍描述:

機器學習正在改變我們理解和與周圍世界互動的方式。本書是您將知識和技能付諸實踐,並使用Python生態系統涵蓋機器學習關鍵領域的完美指南。本書的第二版涵蓋了Python生態系統中的多個函式庫,包括TensorFlow和Keras,以幫助您實現實際的機器學習專案。

本書首先概述了Python中的機器學習。通過複雜的數據集和優化技術的幫助,您將進一步了解如何將高級概念和流行的機器學習演算法應用於實際專案。接下來,您將涵蓋從預測分析到分析股票市場和GitHub存儲庫推薦系統等領域的專案。此外,您還將使用scikit-learn、TensorFlow和Keras等框架在NLP領域上工作,創建自定義新聞提要。在此之後,您將學習如何構建一個高級聊天機器人,並使用PySpark擴展功能。在結尾章節中,您將獲得有關深度學習的令人興奮的見解,並使用計算機視覺和神經網路創建應用程序。

通過閱讀本書,您將能夠無縫分析數據,並通過您的專案產生強大的影響。

您將學到什麼:

- 瞭解Python數據科學堆棧和常用演算法。
- 建立一個模型來預測首次公開募股(IPO)在初始離散交易窗口中的表現。
- 通過創建自定義新聞提要來瞭解NLP概念。
- 創建基於您收藏、關注或派生的GitHub存儲庫的推薦應用程序。
- 獲得使用PySpark從頭開始構建聊天機器人的技能。
- 使用股票數據開發市場預測應用程序。
- 深入研究計算機視覺、神經網路和深度學習等高級概念。

本書適合對機器學習有經驗的從業人員、數據科學家和深度學習愛好者,他們希望通過構建實際專案將他們的機器學習技能提升到更高水平。這本中級指南將幫助您實現Python生態系統中的函式庫,並構建解決各種機器學習領域問題的專案。具備Python編程和機器學習概念的知識將有所幫助。

目錄:

1. Python機器學習生態系統
2. 建立一個尋找低價公寓的應用程式
3. 建立一個尋找廉價機票的應用程式
4. 使用邏輯回歸預測IPO市場
5. 創建自定義新聞提要
6. 預測您的內容是否會爆紅
7. 使用機器學習預測股票市場
8. 使用卷積神經網路進行圖像分類
9. 構建一個聊天機器人
10. 建立一個推薦引擎
11. 下一步是什麼?