***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****

******Free eBook for customers who purchase the print book from Amazon******

---

Are you thinking of learning data science from scratch using Python?(For Beginners)

If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book “Data Analysis from Scratch with Python”, Peters Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain.

From AI Sciences Publisher

Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach, which would lead to better mental representations.

Step By Step Guide and Visual Illustrations and Examples

The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process.

Target Users

Target Users The book is designed for a variety of target audiences. The most suitable users would include:

• Beginners who want to approach data science, but are too afraid of complex math to start
• Newbies in computer science techniques and data science
• Professionals in data science and social sciences
• Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
• Students and academicians, especially those focusing on data science

What’s Inside This Book?

Part 1: Data Science Fundamentals, Concepts and Algorithms

• Introduction
• Statistics
• Probability
• Bayes’ Theorem and Naïve Bayes Algorithm
• Asking the Right Question
• Data Acquisition
• Data Preparation
• Data Exploration
• Data Modelling
• Data Presentation
• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• Semi-supervised Learning Algorithms
• Reinforcement Learning Algorithms
• Overfitting and Underfitting
• Correctness
• The Bias-Variance Trade-off
• Feature Extraction and Selection

Part 2: Data Science in Practice

• Overview of Python Programming Language
• Python Data Science Tools
• Jupyter Notebook
• Numerical Python (Numpy)
• Pandas
• Scientific Python (Scipy)
• Matplotlib
• Scikit-Learn
• K-Nearest Neighbors
• Naive Bayes
• Simple and Multiple Linear Regression
• Logistic Regression
• GLM models
• Decision Trees and Random forest
• Perceptrons
• Backpropagation
• Clustering
• Natural Language Processing

---

Frequently Asked Questions

Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning using Python and further learning will be required beyond this book to master all aspects.

Q: Can I have a refund if this book doesn’t fit for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aisciences.net.

***** 現在購買 (即將回到 24.77 美元) ***** 亞馬遜提供退款保證 (請參閱下方常見問題) *****

****** 購買亞馬遜印刷書的客戶可獲得免費電子書 ******

---

您是否考慮從頭開始學習使用 Python 的資料科學？(適合初學者)

如果您正在尋找一本從頭開始使用 Python 進行資料科學的完整逐步指南，那麼這本書就是為您而寫的。在他的第一本書《使用 Python 從頭開始進行資料分析》取得巨大成功後，彼得·摩根發表了他的第二本書，這次專注於資料科學和機器學習。這被業內人士認為是該領域中最簡單的指南。

由 AI Sciences 出版社提供

我們的書可能是初學者的最佳選擇；這是一本從頭開始學習人工智慧和資料科學的逐步指南。為了更好地理解所涵蓋的概念，建議讀者採取實踐方法，這將有助於更好地形成心智表徵。

逐步指南、視覺化圖解和實例

本書提供了在 Python 中操作、處理、清理、建模和分析數據集的完整指令。這是一本實踐案例研究的實用指南。您將學習到 pandas、NumPy、IPython 和 Jupiter。

目標讀者

本書針對多種目標讀者設計。最適合的讀者包括：

• 想要接觸資料科學，但對於複雜數學感到害怕的初學者


• 計算機科學技術和資料科學的新手


• 從事資料科學和社會科學的專業人士


• 教授、講師或導師，希望以最簡單、最容易理解的方式向學生解釋內容


• 專注於資料科學的學生和學者

本書內容

第一部分：資料科學基礎、概念和演算法

• 介紹


• 統計學


• 機率


• 貝葉斯定理和朴素貝葉斯演算法


• 提出正確的問題


• 資料獲取


• 資料準備


• 資料探索


• 資料建模


• 資料呈現


• 監督式學習演算法


• 非監督式學習演算法


• 半監督式學習演算法


• 強化學習演算法


• 過度擬合和欠擬合


• 正確性


• 偏差-方差平衡


• 特徵提取和選擇

第二部分：實踐中的資料科學

• Python 程式語言概述


• Python 資料科學工具


• Jupyter Notebook


• 數值計算 Python (Numpy)


• Pandas


• 科學 Python (Scipy)


• Matplotlib


• Scikit-Learn


• K-最近鄰算法


• 朴素貝葉斯


• 簡單和多元線性回歸


• 邏輯回歸


• GLM 模型


• 決策樹和隨機森林


• 感知器


• 反向傳播


• 聚類


• 自然語言處理

---

常見問題

Q: 這本書是否包含成為資料科學專家所需的一切？ A: 很抱歉，不包含。本書旨在為初學者提供使用 Python 進行資料科學和機器學習的第一步，要掌握所有方面，需要在本書之外進一步學習。

Q: 如果這本書不適合我，我可以退貨嗎？ A: 是的，亞馬遜會退款，有關亞馬遜退款服務的更多信息，請前往亞馬遜幫助平台。如果您發送電子郵件至 contact@aisciences.net，我們也很樂意為您提供幫助。