Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The hope that comes with this discipline is that including the experience into its tasks will eventually improve the learning. But this improvement needs to happen in such a way that the learning itself becomes automatic so that humans like ourselves don't need to interfere anymore is the ultimate goal. There are close ties between this discipline and Knowledge Discovery, Data Mining, Artificial Intelligence (AI) and Statistics. Typical applications can be classified into scientific knowledge discovery and more commercial ones, ranging from the "Robot Scientist" to anti-spam filtering and recommender systems. But above all, you will know this discipline because it's one of the topics that you need to master if you want to excel in data science. Today's scikit-learn tutorial will introduce you to the basics of Python machine learning: step-by-step, it will show you how to use Python and its libraries to explore your data with the help of matplotlib, work with the well-known algorithms KMeans and Support Vector Machines (SVM) to construct models, to fit the data to these models, to predict values and to validate the models that you have build. The first step to about anything in data science is loading in your data. This is also the starting point of this scikit-learn tutorial.

The answer to the question "What machine learning algorithm should I use?" is always "It depends." It depends on the size, quality, and nature of the data. It depends on what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them.

The goal of this object is to minimise spending attention to irrelevant details and spent time to the problem. Possible model types are continuous, binomial and multinomial. This class and corresponding functionality is object-oriented. This enables the user to focus on the statistics only, instead of paying attention to irrelevant details (how to partition the data, how to handle missing values, etc.). The most popular model classes are already available: generalised linear models (with a stepwise or lasso feature selection), support vector machines, decision trees and neural networks.

Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site effects was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough.

Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. All code is available on Github. I'll have another post on the details of using Scikits and Sklearn. SVM is a supervised machine learning algorithm which can be used for classification or regression problems.

Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.

Imagine a case - if there is no straight line (or hyperplane) which can separate two classes? In the image shown below, there is a circle in 2D with red and blue data points all over it such that adjacent data points are of different colors. SVM handles the above case by using a kernel function to handle non-linear separable data. It is explained in the next section. In simple words, it is a method to make SVM run in case of non-linear separable data points.

Data scientists are professionals who use the most appropriate tools and methodologies to get their jobs done. The best data scientists avail themselves of the complete set of knowledge- and pattern-discovery approaches that involve statistical analysis. How should we refer to the sum total of data science techniques? Often, they are lumped under the term "advanced analytics." This phrase is deliberately vague in that it is intended as a catch-all for everything from statistical analysis and data mining to predictive modeling, natural language processing, support vector machines, and so on.

Advances in theory and computer hardware have allowed neural networks to become a core part of online services such as Microsoft's Bing, driving their image-search and speech-recognition systems. The companies offering such capabilities are looking to the technology to drive more advanced services in the future, as they scale up the neural networks to deal with more sophisticated problems. It has taken time for neural networks, initially conceived 50 years ago, to become accepted parts of information technology applications. After a flurry of interest in the 1990s, supported in part by the development of highly specialized integrated circuits designed to overcome their poor performance on conventional computers, neural networks were outperformed by other algorithms, such as support vector machines in image processing and Gaussian models in speech recognition.

Here you will learn how to define the optimization problem for SVMs when it is not possible to separate linearly the training data.