通过创建超级有趣的项目来了解使用 Python 进行数据科学和机器学习！
你会学到什么
学习创建真实世界的数据科学和机器学习项目
了解不同的机器学习模型和算法
了解数据科学生命周期并应用创建项目的方法
了解数据科学的不同领域：特征工程、特征转换和模型选择
了解自然语言处理
了解人工智能以及如何使用它来解决数据科学问题
课程内容
13 节 • 93 节课 • 19 小时 55 米总长度
要求
Python 编程的基本知识是必不可少的
你应该知道编程的主题，如函数、数据结构和面向对象的编程
描述
数据科学常见问题
什么是数据科学？
数据科学封装了创建以数据为中心的工件和应用程序所需的跨学科活动，以解决特定的科学、社会政治、商业或其他问题。
让我们看看这个语句的组成部分
1. 数据：从人、地点和事物的活动中收集或捕获的信息的可测量单位。
2. 具体问题：为了理解自然、社会或其他现象，我们能否制定具体问题，根据观察、测试和/或数据建模的模式给出的答案是适当的。
3. 跨学科活动：提出问题、评估用于寻找答案的数据和调查结果的适当性需要了解特定学科领域。根据手头的数据确定模型的适当性和从模型中得出的推论需要了解统计和计算方法
为什么选择数据科学？
包含物理、社会、商业和政治领域的粒度、大小和可访问性数据在过去十年或更长时间中呈爆炸式增长。
根据谷歌首席经济学家哈尔瓦里安的说法，我引用
“我一直在说，未来 10 年最性感的工作将是统计学家和数据科学家”
“获取数据的能力——能够理解、处理、从中提取价值、可视化、交流——这将是未来几十年非常重要的技能，不仅在专业人士水平，但即使在小学生、高中生、大学生的教育水平上也是如此。”
************ ************ 课程组织 ************************ **
第 1 部分：设置 Anaconda 和编辑器/库
第 2 节：学习数据科学生命周期和方法论
第 3 节：学习数据预处理：数据的清理、规范化、转换
第 4 节：一些机器学习模型：线性/逻辑回归
第 5 节：项目 1：酒店预订预测系统
第 6 节：项目 2：自然语言处理
第 7 节：项目 3：人工智能
第 8 节：告别
本课程适用于谁
本课程专门针对那些具有一定编程知识并希望了解如何解决数据科学和机器学习问题
的人本课程适用于希望在数据科学和机器学习领域建立职业的人
本课程是想要以完美方式学习数据科学的人：通过学习特征工程：数据清理、转换并将其用于算法
本课程适合想要通过创建有趣的项目来学习机器学习和人工智能的人
Learn about Data Science and Machine Learning with Python by Creating Super Fun Projects!
Video: .mp4 (1280×720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 8.26 GB
Genre: eLearning Video | Duration: 93 lectures (19 hour, 55 mins) | Language: English
What you’ll learn
Learn to create real world Data science and Machine learning projects
Learn about different Machine learning models and algorithms
Learn about Data Science life cycle and apply methodologies for creating projects
Learn about different domains of Data Science: Feature engineering, Feature transformation, and model Melection
Learn about Natural Language Processing
Learn about Artificial Intelligence and how to use it to solve the Data Science problems
Course content
13 sections • 93 lectures • 19h 55m total length
Requirements
Basic knowledge of Python programming is essential
You should know topics of programming like functions, data structures and object oriented programming
Description
FAQ about Data Science
What is Data Science?
Data science encapsulates the interdisciplinary activities required to create data-centric artifacts and applications that address specific scientific, socio-political, business, or other questions.
Let’s look at the constituent parts of this statement
1. Data: Measurable units of information gathered or captured from activity of people, places and things.
2. Specific Questions: Seeking to understand a phenomenon, natural, social or other, can we formulate specific questions for which an answer posed in terms of patterns observed, tested and or modeled in data is appropriate.
3. Interdisciplinary Activities: Formulating a question, assessing the appropriateness of the data and findings used to find an answer require understanding of the specific subject area. Deciding on the appropriateness of models and inferences made from models based on the data at hand requires understanding of statistical and computational methods
Why Data Science?
The granularity, size and accessibility data, comprising both physical, social, commercial and political spheres has exploded in the last decade or more.
According to Hal Varian, Chief Economist at Google and I quote
“I keep saying that the sexy job in the next 10 years will be statisticians and Data Scientist”
“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids.”
************ ************Course Organization **************************
Section 1: Setting up Anaconda and Editor/Libraries
Section 2: Learning about Data Science Lifecycle and Methodologies
Section 3: Learning about Data preprocessing: Cleaning, normalization, transformation of data
Section 4: Some machine learning models: Linear/Logistic Regression
Section 5: Project 1: Hotel Booking Prediction System
Section 6: Project 2: Natural Language Processing
Section 7: Project 3: Artificial Intelligence
Section 8: Farewell
Who this course is for
This course is dedicated to those people who has some knowledge of programming and wants to learn about how to solve data science and machine learning problems
This course is for them who wants to built career in the field of Data science and Machine Learning
This course is for them who wants to learn data science in perfect way: by learning about feature engineering: data cleaning, transforming and using it to algorithms
This course is for them who wants to learn Machine Learning and Artificial Intelligence by creating fun projects
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