What is machine learning?

reentrantlock 2014-07-25 08:45:22
Hi all,
I found a job position which demands applicants to master the following skills:
At Least 3 Years of experience and knowledge of:
- Java
- Scala
- Hadoop
- Online Advertising / Ad Tech experience
- Machine Learning

Who can tell me what machine learning is?
Thanks a lot in advance.
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糯米粽子1218 2016-06-20
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请遵守CSDN用户行为准则，不得违反国家法律法规。 转载文章请注明出自“CSDN（www.csdn.net）”。如是商业用途请联系原作者。
健者天行 2016-06-20
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问阿法狗
司马中达 2016-06-14
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找本图像处理的研究生课程 深挖一下就有了
望断雁南飞 2016-06-14
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XianMing的博客 2016-06-14
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发现好像还是没回答，什么是机器学习。 哈哈哈，具体定义，请google，baidu，教科书。
XianMing的博客 2016-06-14
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1.先回答你的问题，What is machine learning？ 首先这已是一门学科，属于人工智能一个重要部分。 2..这门学科（机器学习）是为了解决什么问题呢？ 让机器（多指计算机）能和人一样学习。 3.这门的具体应用在哪些领域 计算机视觉（人脸识别，图片搜索等）；自然语言处理（语音识别（微软的Cortana），机器翻译等）；社交网络分析（热点发现，用户画像等）；推荐（音乐推荐，网购产品推荐，广告推荐）； 题主的招聘信息，应该是一家要用机器学习来做广告推荐吧。 4.再来回答下，机器学习，数据挖掘（有人提到）等间的关系？ 看这篇就行： http://www.open-open.com/lib/view/open1420615208000.html 5.最后说下学习该课程建议： 你可以学习这个视频，如果记得大学的高数和线代，概率，你会发现其实入门时简单的。下面是coursera里Andrew Ng的课程，浅显易懂。 https://www.coursera.org/learn/machine-learning
灰来灰去的灰机 2014-11-19
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It's about Artificial Neural Network, Fuzzy Logic, Genetic algorithm and other kinds of algorithms. The classic use is to predict house price or something like it.
InitialJ 2014-11-19
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机器学习普通程序员用不到， 几个应用：微软Cortana、Kinect人体识别追踪、Google Brain。。。
技术图腾 2014-07-29
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楼主连ml是啥都不知道，就别惦记这个岗位了
卧_槽 2014-07-29
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Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit rule-based algorithms is infeasible for a variety of reasons. Example applications include spam filtering, optical character recognition (OCR),[1] search engines and computer vision. Machine is sometimes conflated with, and sometimes distinguished from data mining and pattern recognition.[citation needed] Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning, in particular classification, where the learning algorithm is presented with email (or other) messages labeled beforehand as "spam" or "not spam", to produce a computer program that labels unseen messages as either spam or not.
reentrantlock 2014-07-26
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引用 23 楼 u013192985 的回复:
[quote=引用 22 楼 bfdeh 的回复:] I think it's some kind of human intelligent.
Do you mean it is a branch of artificial intelligence? Would you pls.explain it exactly and specifically to make it easy to understand? Thanks a lot in advance.[/quote] It's better with an example to illustrate.
reentrantlock 2014-07-26
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引用 22 楼 bfdeh 的回复:
I think it's some kind of human intelligent.
Do you mean it is a branch of artificial intelligence? Would you pls.explain it exactly and specifically to make it easy to understand? Thanks a lot in advance.
盐酸地芬尼多片 2014-07-25
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Kenmu-啊武 2014-07-25
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引用 17 楼 u013192985 的回复:
[quote=引用 16 楼 huangjianwu 的回复:] 机器学习这门学问，应该是“人工智能”、“数据挖掘”方面的意思！
Many of programmers seem not to be familiar with this.[/quote] Yes, it's a professional knowledge so that programmers must spend a lot of time to understand it. 不装X，在水区，还是说母语吧！
查看余e 2014-07-25
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每天回帖即可获得10分可用分！
reentrantlock 2014-07-25
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引用 16 楼 huangjianwu 的回复:
机器学习这门学问，应该是“人工智能”、“数据挖掘”方面的意思！
Many of programmers seem not to be familiar with this.
Kenmu-啊武 2014-07-25
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机器学习这门学问，应该是“人工智能”、“数据挖掘”方面的意思！
其实我是真性情 2014-07-25
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引用 14 楼 sinat_17738681 的回复:
引用 2 楼 lwb314 的回复:
都说沙发难抢，我来试试
虽然偶尔过来看看 但是你已经好眼熟了啊
sinat_17738681 2014-07-25
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引用 2 楼 lwb314 的回复:
都说沙发难抢，我来试试
虽然偶尔过来看看 但是你已经好眼熟了啊
sinat_17738681 2014-07-25
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表示英文已经敲好，但是看到#11、、、 为什么不谷歌一下，建议顺便谷歌一下数据挖掘
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什么是机器学习? What is machine learning?
什么是机器学习__What_is_machine_learning_
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基于Python的机器学习（Python-Real World Machine Learning）-2016年最新英文原版，0积分——全书983页，内容很丰富。 What this learning path covers？ Module 1, Python Machine Learning Cookbook, teaches you about the algorithms that we use to build recommendation engines. We will learn how to apply these algorithms to collaborative filtering and movie recommendations. Module 2, Advanced Machine Learning with Python, explains how to apply several semi-supervised learning techniques including CPLE, self learning, and S3VM. Module 3, Large Scale Machine Learning with Python, covers interesting deep learning techniques together with an online method for neural networks. Although TensorFlow is only in its infancy, the framework provides elegant machine learning solutions.
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Python Machine Learning By Example by Yuxi (Hayden) Liu English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book Description Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds About the Author Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast. Table of Contents Getting Started with Python and Machine Learning Exploring the 20 newsgroups data set Spam email detection with Naive Bayes News topic classification with Support Vector Machine Click-through prediction with tree-based algorithms Click-through rate prediction with logistic regression Stock prices prediction with regression algorithms Best practices
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