The first year of the advent of artificial intelligence, big data, which acts as what role? Data analysis is what positioning? Professional distinction?
A white look instantly understandable picture
Simple problem?
The world's largest travel rental community in 2011, Airbnb was entangled in new subscriber growth slow, one day, their data analysis team found that the extent of Listing beautiful photos, with a predetermined number of listing to large positive correlation. So they put forward the hypothesis that "with professional photographs listings to be more sought-after, so homeowners will certainly be willing to apply for this service Airbnb offers." They quickly on the line to provide a professional photography service photo version, then do A / B Test with the original version and found the same listings, do not use more than 2-3 times more orders than using a professional photography service.
Complex issues?
After 2010, the era of the rise of the portal Netease, Sohu, Tencent Big Three transition to a mobile terminal, a virtual monopoly on the market was news client. And just two years later, the headlines today, using "machine learning" This is the Dragon Sword of interest to users of personal recommendation Usenet, breaking the monopoly giant, the news client boss. Although later Tencent and Netease To counter the headlines, we launched a similar product daily newsletter and Netease number, but the late start, and algorithm immature, have failed.
Vernacular summary
Like several points praise, comments, collection number, this is a simple analysis of the total amount of reading. Analysis of like, "You might be interested in people" is a complex analysis is required to find a machine learning algorithm, similar to watercress recommend movies you are interested, Taobao commodity you recommend interested.
Written definitions
1) refers to artificial intelligence machine like people to decision-making
2) is a machine learning technology of artificial intelligence
3) the depth of learning is a branch of machine learning. Focus on neural network algorithm.
4) Data analysis is the basis of learned data analysis method for processing data, you can understand the knowledge of machine learning.
人工智能是一类非常广泛的问题，机器学习是解决这类问题的一个重要手段，深度学习则是机器学习的一个分支。大数据是人工智能的基础，机器学习使大数据转变为知识或生产力。
深度学习它除了可以学习特征和任务之间的关联以外，还能自动从简单特征中提取更加复杂的特征。在很多人工智能问题上，深度学习的方法突破了传统机器学习方法的瓶颈，推动了人工智能领域的快速发展。
数据分析可以帮助你从零进入人工智能时代：如果你喜欢深入技术，学会了数据分析，可以去学习机器学习。如果你喜欢商业方面的内容，可以往人工智能业务方向发展。
关于数据挖掘
随着2006年以Hadoop为代表的大数据技术的蓬勃兴起，解决了数据库时代的数据存储和处理能力的不足限制；云计算技术的大规模应用，比如Amazon和阿里云为代表的云计算厂商，将处理能力和计算能力的成本大大降低，从而让大规模的集群计算系统变得廉价；从而将针对数据的分析拓展至全量的数据分析，而非数据抽样。另外一个方面是将从前在数据挖掘时代无法应用的算法和思路变成了可能。这个时代ML(Machine Learning)机器学习逐渐取代数据挖掘，成为火热的关键词。
那机器学习与数据挖掘的关系是什么呢？机器学习是建立在数据挖掘技术之上发展而来，数据挖掘的概念更广，机器学习只是数据挖掘领域中的一个新兴分支与细分领域，只不过基于大数据技术让其逐渐成为了当下显学和主流。
数据分析是把数据变成信息的工具，数据挖掘是把信息变成认知的工具，如果我们想要从数据中提取一定的规律往往需要数据分析和数据挖掘结合使用。下面是一个比较弱的例子：
举个例子：你有50块钱，去买菜，经过一一问价，你知道了50块钱能买多少蔬菜，能买多少肉，能吃多少天，心里得出一组信息，这就是数据分析。根据自己的偏好，营养价值，用餐时间计划，最有性价比的组合确定了一个购买方案，这就是数据挖掘。
人工智能相关企业
https://blog.csdn.net/luanpeng825485697/article/details/78769184
Several good quality paper
Statistical data analysis and data mining What's the difference?
http://www.duozhishidai.com/article-11047-1.html
Clustering algorithms and data mining advantages
http://www.duozhishidai.com/article-12942-1.html
How self-study, data mining has become the "master"?
http://www.duozhishidai.com/article-9796-1.html
Data analysis and data mining of difference and contact?
http://www.duozhishidai.com/article-9800-1.html
Build a data mining model, which is divided into steps?
http://www.duozhishidai.com/article-9719-1.html