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As per Pedro Domingos in his famous paper "A Few Useful Things to Know about Machine Learning" he writes Machine learning systems automatically learn programs from data.
But from my experience we r giving algorithms like ANN or SVM etc.
My question is how it is automating automation?
Could someone put some light with example.
When you develop a machine learning algorithm, with ANN or SVM or whatever, you don't say to your programming how to solve your problem, you are telling him how to learn to solve the problem.
SVM or ANN are ways to learn a solution to a problem, but not how to solve a problem.
So when people say "Machine learning systems automatically learn programs from data", they are saying that you never programmed a solution to your problem, but rather letting the computer learning to do so.
To quote wikipedia : "Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed"
https://en.wikipedia.org/wiki/Machine_learning
[Edit]
For example let's take one of the most simple machine learning algorithm, the linear regression in a 2D space.
The aim of this algorithm is to learn a linear function given a dataset of (x,y), so when you given your system a new x you get an approximation of what the real y would be.
But when you code a linear regression you never specify the linear function y = ax+b. What you code is a way for the program to deduce it from the dataset.
The linear function y=ax+b is the solution to your problem, the linear regression code is the way you are going to learn that solution.
https://en.wikipedia.org/wiki/Linear_regression
Machine Learning development helps to improve business operations as well as improve business scalability. A number of ML algorithms and artificial intelligence tools have gained tremendous popularity in the community of business analytics. There has been a rise in machine learning market due to faster and cheaper computational processing, easy availability of data as well as affordable data storage.
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I am trying to make a binary classifier using machine learning and I am trying to develop other features for my data using correlated features (numerical attributes) I have. I searched much but could not get a block of code that will work with me.
What should i do?
I've searched in dimenshionality reduction and found library (Multivariate Statistics) but actually i did not understand and i felt lost :D
No one will make a choice for you what exact method to choose. They are many, many different ways of doing a binary classification and to do feature extraction. If you feel overwhelmed by all these names that libraries such as Multivariate Statistics offer, then take a look at a textbook on statistics and machine learning, understanding the methods is independent from the programming language.
Start with some simple methods such as principal compenent analysis (PCA), (MultivariateStats.jl provides that), then test others as you gain more knowledge on your data and the methods.
Some Julia libraries to take a look at: JuliaStats (https://github.com/JuliaStats) with its parts
StatsBase for the most basic stuff
MultivariateStats for methods like PCA
StatsModels (and DataFrames) for statistical models
many more ....
For Neural Networks there are Flux.jl and KNet.jl
For Clustering there is Clustering.jl
Then, there are also bindings to the python libraries Tensorflow (Neural Networks & more) and Scikit-Learn (all kinds of ML algorithms)
There are many more projects, but these are some that I think are important.
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What is the difference between Machine Learning and Computer Vision?
I am studying Machine learning now, for 1 week and still don't know what is different between them?
Will you prefer axe to cut an apple? even a simple knife is enough for it!
Will you prefer sword to sew a pyjama? a short needle is enough for it!
Same is the case of comments made above.
Computer vision do deals with image recognition too, but you don't need it for simple face recognition project. It is a basic project of machine learning and is available on many GitHub kind of websites for free. So, you don't need to learn "computer vision" especially to build a face recognition system.
Computer vision is a good field, but machine learning is sufficient for face recognition!
Generally speaking computer vision is a field that uses some machine learning techniques to solve problems related to the field, that is, making a computer recognize images and identify what's in them!
Machine learning:
Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed.
Example:
When we coming to the computer, Writing a peace of code or program and telling the computer step by step to do. But ML we don't do that, the system learns on its own. We just provide the past data(called labelled data) and the system learns during the process what is known as training process, we tell the system the system the outcome are right or wrong, that feedback is taken by system and it corrects itself and that's who its learns, it gives the correct output of the most of the cases. Obviously it is not 100% correct but aim is to get as accurate as possible.
Computer vision:
Computer vision is nothing but dealing with the digital images and videos in the computer. Computer vision is evolving rapidly day-by-day. Its one of the reason is deep learning. When we talk about computer vision, a term convolutional neural network( abbreviated as CNN) comes in our mind because CNN is heavily used here. Examples of CNN in computer vision are face recognition, image classification etc. It is similar to the basic neural network. CNN also have learn able parameter like neural network i.e, weights, biases etc.
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I practiced already some machine learning aspects, and developed some small projects. Nowadays some blogs, articles, open posts talk about deep learning. I get interested to see practically what the difference between machine learning and deep learning is, and maybe to learn a new approaches/ techniques called deep learning. I read few blogs, but conceptually I see that deep learning is a subset of machine learning, and it’s nothing more than Neural networks with multiple layers!!
I am however stunned and perplexed to recognize if it is the only difference between machine learning and deep learning !!!
What is the merit to think of deep learning and not machine learning if we want only talk about neural networks? so if it is, why not call it neural networks, or deep neural networks to distinguish some classification ?
Is there a real difference than that I mentioned?
Does there any practical example showing a significant difference letting us to make these different notions?
Deep learning is set of ML patterns and tactics to increase accuracy of classical ML algorithms, like MLP, Naïve Bayes classifier, etc.
One of the earliest and easiest of such tactics – adding hidden layers to increase network’s learning capacity. One of recent - convolutional autoencoder
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I have a pretty simple question. However I have searched extensively and am unable to find the answer. Is a genetic algorithm considered to be a form of unsupervised learning? I know that the algorithms evolves independently, however the fitness of each individual in the population is regularly measured (supervised?).
The objective of my algorithm is to optimize a set of heuristic weights via a genetic algorithm.
Thank you for your help!
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Genetic Algorithms can be used for both supervised and unsupervised learning, e.g.:
Unsupervised Genetic Algorithm Deployed for Intrusion Detection, (2008).
Zorana Banković,
Slobodan Bojanić,
Octavio Nieto,
Atta Badii.
If you have labeled training data or tagged examples, then you are using supervised training.
From http://en.wikipedia.org/wiki/Unsupervised_learning
In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.
From which it's pretty clear that genetic algorithms are not unsupervised as they are measured against a fitness criteria. Individual mutations may not be supervised, but the system as a whole is supervised as mutations are either removed or built upon based on the resulting fitness they give the algorithm.
From http://en.wikipedia.org/wiki/Reinforcement_learning
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, statistics, and genetic algorithms.
Which would sort of suggest that genetic algorithms are considered to fall under reinforcement learning.
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I am new at the domain of machine learning and i have noticed that there are a lot of algorithms/ set of algorithms that can be used: SVM, decision trees, naive bayes, perceptron etc...
That is why I wonder which algorithm should one use for solving which issue? In other words which algorithm solves which problem class?
So my question is if you know a good web site or book that focuses on this algorithm selection problematic?
Any help would be appreciated. Thx in advance.
Horace
Take Andrew Ng's machine learning course on coursera. It's beautifully put together, explains the differences between different types of ML algorithm, gives advice on when to use each algorithm, and contains material useful for practioners as well as maths if you want it. I'm in the process of learning machine learning myself and this has been by far the most useful resource.
(Another piece of advice you might find useful is to consider learning python. This is based on a mistake I made of not starting to learn python at an earlier stage and ruling out the many books, web pages, sdks, etc that are python based. As it turns out, python is pretty easy to pick up, and from my own personal observations at least, widely used in the machine learning and data science communities.)
scikit-learn.org published this infographic, that can be helpful, even when you're not using sklearn library.
#TooTone: In my opinion Machine Learning in Action could help the OP with deciding on which technique to use for a particular problem, as the book gives a clear classification of the different ML algorithms and pros, cons, and "works with" for each of them. I do agree the code is somewhat hard to read, especially for people not used to matrix operations. There is years of research condensed into a 10 line Python program, so be prepared that understanding it will take a day (for me at least).
It is very hard answer the question “which algorithm for which issue?”
That ability comes with a lot of experience and knowledge. So I suggest, you should read few good books about machine learning. Probably, following book would be a good starting point.
Machine Learning: A Probabilistic Perspective
Once you have some knowledge about machine learning, you can work on couple of simple machine learning problems. Iris flower dataset is a good starting point. It consists of several features belonging to three types of Iris species. Initially develop a simple machine learning model (such as Logistic Regression) to classify Iris species and gradually you could move to more advanced models such as Neural Networks.
As a simple starting place I consider what inputs I have and what outputs I want, which often narrows down choices in any situation. For example, if I have categories, rather than numbers and a target category for each input, decision trees are a good idea. If I have no target, I can only do clustering. If I have numerical inputs and a numerical output I could use neural networks or other types of regression. I could also use decision trees that generate regression equations. There are further questions to be asked after this, but it's a good place to start.
Following DZone Refcard might also helpful .. http://refcardz.dzone.com/refcardz/machine-learning-predictive. But you will have to dig in to each in detail eventually.