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I have recently started studying Machine Learning and found that I need to refresh probability basics such as Conditional Probability, Bayes Theorem etc.
I am looking for online resources where I can quickly brush up probability concepts wrt Machine Learning.
The online resorces, I stumbled upon are either very basic or too advanced.
This might help: http://www.cs.cmu.edu/~tom/10601_fall2012/lectures.shtml
The above link is from Tom Mitchell's Machine Learning Class # CMU. Videos are available too. You will gain a very good understanding of ML concepts if you go through all the videos. (or just the first few videos for Conditional Probability, Bayes Theorem, etc).
The notion of conditional probability and bayes theorem are very basic themselves. It doesn't get any more basic than that in probabilistic modeling, you might say. Which suggests that you didn't look two well at what you've found or didn't really do any search at all.
Off the top of my head, I can name two resources: first, any Coursera course dealing with probabilities or machine learning (see AI, Statistics One or Probabilistic Graphical Models) contains these preliminaries. Second, there's a number of books on statistics freely available online, one example being Information Theory, Inference, and Learning Algorithms.
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Thanks for making it this far on my post!
I am studying engineering, yet have a passion for programming and wish to implement computer science knowledge into my own research.
My question is pertaining to any resources that this community has available and any advice you all are willing to give regarding getting started in this broad field.
I’m mainly confused about ‘neural networks’ in relation to Deep Learning as well as implementation of algorithms.
I have slight Python and R knowledge.
Note: one of the subfora of StackExchange is probably a better fit for this question.
In any case, for ML you can do just fine with basic Python/R. Most of the research and work done on ML is based on TensorFlow and similar frameworks currently (2018). To use the frameworks you don't really need a strong programming background to setup and train models on them (although it certainly helps). Actually, math/statistics will help you more, specially if you want to get to the bottom of it (i.e. reading the latest articles/papers, etc.).
Mainly I’m confused about ‘neural networks’ in relation to Deep Learning
"Deep Learning" is basically taking advantage of modern computing capabilities to train complex models (e.g. neural networks with many hidden layers) which a few years ago (e.g. 10 years ago) were unfeasible. Informally speaking, the more complex your network is, the more interesting are the things that it can learn.
as well as implementation of algorithms.
Typically, you will use an existing framework -- you won't implement the algorithms yourself. Although, of course, implementing a MultiLayer Perceptron by yourself is always a good and fun learning exercise.
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Any tools could help recognize the data distribution pattern, and then make the decision to choose ML algorithms?
Firstly, you have to understand Machine Learning as a field, and have some understanding of its sub fields. If you don't intuitively understand your tools, you won't be able to identify when to use them.
The idea you're talking about is called exploratory data analysis, and it can be very approachable if you think about it the right way. Think about it in terms of the scientific method:
First, look over the data, and any documentation about it.
Then, come to some hypotheses about the patterns that might exist.
Based on your understanding of ML, brainstorm some approaches that might give some insight into your hypotheses. For example, if you see that your proposed dependent value can have several distinct values, you have a classification problem, and based on your input data, you should choose an appropriate approach.
The tools that you might find useful are plentiful, but a good start could be the programming language R, or Python. Both are very strong data science tools. R has a greater learning curve, but is built with data science in mind. Python, on the other hand, is very easy to pick up, but you have more choices to make with regards to ML and data science libraries. With Python, look into Pandas for CSV and data manipulation, and Tensorflow, Theano or Scikit-Learn for data analysis and ML.
Hope this helps!
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I am keen in learning machining learning. I know programming, just want to know some useful sites which will help in understanding the concepts of machine learning with simple examples.
As a beginner in Machine Learning you should start with the book
Pattern Recognition and Machine Learning, by Christopher M. Bishop
There are some prerequisites other than programming are Linear Algebra, Probability theory, etc. i.e. you should have a strong background in Mathematics. Although the book I suggested covers the common mathematical frameworks needed for understanding Machine Learning in its introductory chapters.
Moreover, you should practice implementing different learning algorithms (start from smaller ones) to grab the concepts well. As Andrej Karpathy said,
...everything became much clearer when I started writing code.
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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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.