Data Science Algorithms in a Week

David Natingga

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商品描述

Key Features

  • Get to know seven algorithms for your data science needs in this concise, insightful guide
  • Ensure you're confident in the basics by learning when and where to use various data science algorithms
  • Learn to use machine learning algorithms in a period of just 7 days

Book Description

Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.

This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.

This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.

What you will learn

  • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems
  • Identify a data science problem correctly and devise an appropriate

商品描述(中文翻譯)

主要特點

- 在這本簡潔而深入的指南中,了解七種適用於數據科學需求的算法。
- 通過學習何時以及在哪裡使用不同的數據科學算法,確保您對基礎知識有信心。
- 在短短七天內學習使用機器學習算法。

書籍描述

機器學習應用程序高度自動化且自我修改,隨著更多數據的學習,它們在時間上持續改進,幾乎不需要人為干預。為了應對各種現實世界數據問題的複雜性,已經開發出了專門解決這些問題的機器學習算法。數據科學通過算法和統計分析,幫助您從現有數據中獲取新知識。

本書將解決與準確和高效的數據分類和預測相關的問題。在七天的學習過程中,您將介紹七種算法,並通過練習幫助您學習機器學習的不同方面。您將了解如何對數據進行預分群,以優化和分類大型數據集。然後,您將了解如何根據數據集中的現有趨勢預測數據。

本書涵蓋的算法包括:k-最近鄰算法、朴素貝葉斯算法、決策樹算法、隨機森林算法、k-均值算法、回歸算法和時間序列算法。完成本書後,您將了解選擇哪種機器學習算法用於分群、分類或回歸,以及哪種算法最適合解決您的問題。

您將學到什麼

- 了解如何使用朴素貝葉斯、決策樹和隨機森林進行分類,以實現準確性,解決複雜問題。
- 正確識別數據科學問題並制定適當的解決方案。