Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2/e

David Natingga

相關主題

商品描述

Build a strong foundation of machine learning algorithms in 7 days

Key Features

  • Use Python and its wide array of machine learning libraries to build predictive models
  • Learn the basics of the 7 most widely used machine learning algorithms within a week
  • Know when and where to apply data science algorithms using this guide

Book Description

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.

Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.

By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem

What you will learn

  • Understand how to identify a data science problem correctly
  • Implement well-known machine learning algorithms efficiently using Python
  • Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy
  • Devise an appropriate prediction solution using regression
  • Work with time series data to identify relevant data events and trends
  • Cluster your data using the k-means algorithm

Who this book is for

This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set

Table of Contents

  1. Classification using K Nearest Neighbors
  2. Naive Bayes
  3. Decision Trees
  4. Random Forests
  5. Clustering into K clusters
  6. Regression
  7. Time Series Analysis
  8. Python Reference
  9. Statistics
  10. Glossary of Algorithms and Methods in Data Science

商品描述(中文翻譯)

建立機器學習演算法的堅實基礎,七天學會

主要特點

- 使用Python及其廣泛的機器學習函式庫來建立預測模型
- 在一週內學習七種最廣泛使用的機器學習演算法的基礎知識
- 透過本指南,了解何時何地應用資料科學演算法

書籍描述

機器學習應用高度自動化且自我修改,並且在最小人為干預下隨著時間不斷改進,因為它們從訓練資料中學習。為了應對各種現實世界的複雜資料問題,已經開發了專門的機器學習演算法。通過演算法和統計分析,這些模型可以利用現有資料獲取新知識。

《一週內學會資料科學演算法》解決了所有與準確和高效的資料分類和預測相關的問題。在七天的學習過程中,您將介紹七種演算法,並通過練習幫助您了解機器學習的不同方面。您將看到如何對資料進行預分群以優化和分類大型資料集。本書還指導您根據資料集中的現有趨勢預測資料。本書涵蓋的演算法包括k最近鄰、Naive Bayes、決策樹、隨機森林、k-means、回歸和時間序列分析。

通過閱讀本書,您將了解如何選擇適用於分群、分類和回歸的機器學習演算法,並知道哪種演算法最適合您的問題。

您將學到什麼

- 正確識別資料科學問題的方法
- 使用Python高效實現著名的機器學習演算法
- 使用Naive Bayes、決策樹和隨機森林對資料集進行準確分類
- 使用回歸找到適當的預測解決方案
- 使用時間序列資料識別相關資料事件和趨勢
- 使用k-means演算法對資料進行分群

本書適合對Python和統計學有一定背景的資料科學初學者。如果您目前在某種程度上使用資料科學演算法並希望擴展您的技能,本書也對您有所幫助。

目錄

1. 使用K最近鄰進行分類
2. Naive Bayes
3. 決策樹
4. 隨機森林
5. 將資料分為K個群
6. 回歸
7. 時間序列分析
8. Python參考
9. 統計學
10. 資料科學中的演算法和方法詞彙表