This is a difficult article to write because I hope this article will inspire learners. I sit down in front Nike Air Max of a blank page, and ask yourself a difficult question: what kind of library, courses, papers and books are the best machine learning for beginners. Article, in the end what to write, do not write anything, this issue really makes me worry. I have to put myself as a programmer and a machine learning for beginners, stand at this point of view to consider the most appropriate resources. I find the most suitable for each type of resource. If you are a true beginner, and willing to begin to understand the relevant knowledge in the field of machine learning, I hope you can find useful information in my article. My suggestion is to pick out one, a book or a library, or a serious study repeatedly read all the tutorials. Pick one and keep learning until you fully grasp, and then choose a new, repeat the learning process. Now let's get started! Programming Libraries Programming Library Resources I'm a 'learn to take risks and try to' advocates ideas. This is my way to learn programming, I believe a lot of people the same way learning process design. Limit your ability to understand, Air Max 2011 Womens Grey Green then to expand your abilities. If you know how to program, you can quickly learn programming experience to in-depth study on machine learning. Before you implement an actual production system, you must follow some rules, learn the relevant mathematical knowledge. Find a library and read the documentation carefully, according to the curriculum, I began to try to achieve something. Listed below are open source machine learning library Several best. I think, not each of them are suitable for use in your system, but they are you learning, exploration and experimentation of good material. You can begin to learn from one you are familiar with the language from the library, and then go to learn other powerful library. If you are a good programmer, you'll know how to get from one language, simple and reasonable way to migrate to another language. Logical language Nike Dunk Heels is the same, but slightly different syntax and API. R Project for Statistical Computing: This is a development environment that uses a scripting language similar to Lisp. In this library, all functions associated with the statistics you want are provided by the R language, including some complex icon. CRAN (you can think of a third-party package machine learning brother) code machine learning catalog, by statistical techniques and methods and other related fields leader prepared. If you want to experiment, or the rapid expansion of knowledge, R language is a must to learn. But it may not be your first stop learning. WEKA: This is a data mining workbench, to provide users with a range of data mining the number of the whole process of API, command-line and graphical user interface. You Air Jordan Heels can prepare the data, visualization, establish classification, regression analysis, cluster model, while other algorithms can be executed by a third-party plug-ins. In addition WEKA, Mahout is a good JAVA Hadoop framework for machine learning offer, adidas adiPure Crazyquick Collegiate Royal/White Q33301 Outlet you can learn on their own. If you are a machine learning and Big Data learning novice, then insist on learning WEKA, and dedication to learning a library. Scikit Learn: What is written in Python, based on machine learning library NumPy and SciPy of. If you are a Python or Ruby language programmers, it is suitable for your use. This library is very user-friendly interface, powerful, and with a detailed description of the document. If you want Nike Jordan Melo B Mo to try another library, you can Nike Heels Boots choose Orange. Octave: If you are very familiar with MatLab, or you are seeking to change the NumPy programmer, you can consider Octave. This is a numerical computing environment and MatLab like, with Octave you can easily solve linear and nonlinear problems, such as machine learning algorithms underlying issues involved. If you have engineering background, so you can thus start. BigML: Maybe you do not want to carry out programming. You can not pass the code to use WEKA as tools. By using the service you BigMLS to more in-depth work. BigML through Web pages, providing a machine learning interface, so you can be modeled through a browser. Pick out a platform and use it in your practical learning machine learning time. Do not empty talk, go to practice! Video Courses Video Courses machine came into contact with a lot of people are learning through video resources. I watch a lot of YouTube and VideoLectures in machine learning related Nike Kobe VIII video resources. The problem with this is that you could just watch the video and do not actually do it. My suggestion is that you watch the video, should be more notes, timely then you will abandon your notes. Meanwhile, I suggest you learned into practice. Frankly, I did not see particularly suitable for beginners video resources. Video resources are necessary for you to have a certain linear algebra, probability theory and other knowledge. Andrew Ng explained at Stanford may be the most suitable for beginners, I recommend the following are some video resources. Stanford Stanford Machine Learning Machine Learning Course: can be viewed on Coursera, this course is Andrew Ng explain. Just register and you can watch all the courses video, download handouts and notes from Stanford CS229 course. This course includes homework and quizzes, course mainly on the knowledge of linear algebra, using Octave library. California Institute of Technology Caltech Learning from Data Data Analysis courses: you can learn this course on edX, taught by Yaser Abu-Mostafa explain. All course videos and materials are in the California Institute of Nike Free Run 3 Women Technology's website. And Stanford's curriculum is similar to that you can arrange the learning progress according to their own circumstances, to complete homework and small papers. It is similar with the Stanford course topics, paid more attention to detail and mathematical knowledge. For starters, homework may be slightly more difficult. Machine Learning Category on VideoLectures.Net site machine learning directory: This is a very easy dazzling repository. You can find out more interested in resources and in-depth study. Do not entangled in the video is not for you, or for the content of interest that you can take notes. I myself would have been repeated in-depth study of some problems, but to find new topics to learn. In addition, on this site you can find is a master in this field is what. 'Getting In Shape For The Sport Of Data Science' - taught by a Jeremy Howard: This is the dialogue and machine learning competitors, they are some of the practices of the R language users. This is a very precious resource, because very few people will discuss the issue of a complete study of the process and exactly what to do. I had fantasized online to find a TV show, recording the whole process of machine Air Max 2011 Womens Red learning competition. This is what I started learning machine learning experience! Overview Papers Review papers if you're not used to reading scientific papers, you'll find articles of language is very obscure. This paper is like a fragment of a textbook, but Nike Free Run 3 the paper will introduce an experiment 2015 Nike Free 5.0 or other frontier areas of knowledge. However, if you are ready to start learning from reading the paper machine learning, you can still find some very interesting article. The Discipline of Machine Learning Machine Learning Rules: Nike Free Run 3 Women This edited by Tom Mitchell of the White Paper, which defines the rules for machine learning. Mitchell convince CMU president when there is a problem within a hundred years to establish an independent machine learning department, also used in this book point of view. A Few Useful Things to Know about Machine Learning: This is a very good paper, because New Nike Free Run 3 Shoes Silver 3 it details the algorithm, but also raised some very important questions, such as: select characteristics of generalization, model simplification. I just listed two important paper, because reading the paper will make you into trouble. Beginner Machine Learning Books to machine learning book for beginners learning about the machine There are many books, but almost no tailored for beginners. What kind of people are a beginner it? The most likely scenario is that you totally different from other areas such as: computer science, programming or statistics, came to the field of machine learning. So, most of the books require you to have some basis of linear algebra and probability theory. However, there are some books by explaining minimal learning algorithm to encourage programmers to machine learning, the book will be used to introduce some tools, programming libraries to allow programmers Air Jordan Outlet to try. One of the most representative of the book is: 'Programming Collective Intelligence', 'Machine Learning for Hackers', 'Hackersand