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How Is Python Different Than Any Other Programming Languages?

Among many of the programming languages available today, Python is one such option that is in high demand by now. The increasing demand for the language is because it is entirely different than many other programming languages available in the market. One such difference or rather benefit is that Python is quite easy to understand and relatively simple to learn. Do you want to learn Python from scratch? Then Intellipaat Python Course is for you. Typically, selecting a proper programming language depends on many factors, such as training, availability, cost, emotional attachment, and prior investments. But these factors are also variable, and hence there are other factors on which the selection of the correct programming language highly depends. Some of the programming languages available in the market now are Java, PHP, C++, Perl, Ruby, Javascript, TCL, Smalltalk, and many others. Python is very much different than these languages in many ways.

Scale up Big Data mining with Hadoop machine learning tools

Each human or organization doing various activities are constantly generating massive amounts of data. In real life, when you visit supermarkets, doctors, institutions, log into a bank account, visit webpages, spend time in social networks, buy online – you leave a footprint of data. The data of your past carries lots of interesting information about your human habits, interests, behavior patterns. If we scale up to organizations, where every process and decision play a significant role in business success, data becomes a valuable asset. Collected and properly mined historical data may help make critical decisions for the future, optimize the structure, and even see the business trends. Hadoop machine learning tools Big Data is everywhere and, so storing analyzing it becomes a challenge. No human can handle and effectively analyze vast amounts of data. This is where machine learning and distributed storage comes in handy. Hadoop machine learning is an excellent concept for dealing with large amounts of data. The Apache-based Hadoop platform is based on open source tools and utilities that use a network of lots of computers to store and process large amounts of data more efficiently. Hadoop machine learning has joined the concept of different tools. Hadoop…

Feature extraction from retina vascular images for classification

Classifying medical images is a tedious and complex task. Using machine learning algorithms to assist the process could be a huge help. There are many challenges to making machine learning algorithms work reliably on image data. First of all, you need a rather large image database with ground truth information (expert’s labeled data with diagnosis information). The second problem is preprocessing images, including merging modalities, unifying color maps, normalizing, and filtering. This part is essential and may impact the last part – feature extraction. This step is crucial because on how well you can extract informative features depends on how well machine learning algorithms will work. Dataset To demonstrate the classification procedure of medical images, the ophthalmology STARE (Structured Analysis of the Retina) image database was pulled from https://cecas.clemson.edu/~ahoover/stare/. The database consists of 400 images with 13 diagnostic cases along with preprocessed images. For classification problem, we have chosen only vessels images and only two classes: Normal and Choroidal Neovascularization (CNV). So the number of images was reduced to 99 where 25 were used as test data and 74 as training.

Running remote host Weka experiments

Previously, we tried to run a weka server to utilize all cores of the processor in classification tasks. But it appears that the weka server works only in explorer for classification routines. For more advanced machine learning, there is a more flexible tool – experimenter. Weka server doesn’t support this area. So what to do if you want more performance or utilize the multi-core processor of the local machine. There is a way out, but it is quite tricky. Weka has the ability to perform remote experiments that allow spreading the load across multiple host machines that have Weka set up. You can read the documentation of remote experiments here, but it may be somewhat confusing. It took time for me to figure out some parts by trial and error. The trickiest part is to set everything up and prepare the necessary command to be run before performing a remote experiment. So let’s get to it.

Utilizing multi-core processor for classification in WEKA

Currently, WEKA is one of the most favorites machine learning tools. Without programming skills, you can do severe classification, clustering, and extensive data analysis. For some time, I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since research are becoming more complex by using ensemble, voting, and other meta-algorithms that generally are based on multiple classifiers running simultaneously, the performance issues start becoming annoying. You need to wait for hours until the task is completed. The problem is that when running classification algorithms from the WEKA GUI, they utilize a single core of your processor. Such algorithms as Multi-layer Perceptron running 10-fold cross-validation is calculating one cross-fold at the time on one core, taking a long time to accomplish: So I started looking for options to make it use all cores of the processor as separate threads for each operation fold. There are a couple of options available to do so. One is to use WekaServer package, and another is remote host processing. This time we will focus on WekaServer solution. The idea is to start a WEKA server as a distributed execution environment. When starting the server, you can indicate how many cores you…

Regularized Logistic regression

Previously we have tried logistic regression without regularization and with the simple training data set. But as we all know, things in real life aren’t as simple as we would want to. There are many types of data available that need to be classified. A number of features can grow up hundreds and thousands while a number of instances may be limited. Also in, many times, we might need to classify into more than two classes. The first problem that might arise due to many features is over-fitting. This is when learned hypothesis hΘ (x) fit training data too well (cost J(Θ) ≈ 0), but it fails when classifying new data samples. In other words, the model tries to distinct each training example correctly by drawing very complicated decision boundaries between training data points. As you can see in the image above, over-fitting would be green decision boundary. So how to deal with the over-fitting problem? There might be several approaches: We leave first two out of the question because selecting an optimal number of features is a different topic of optimization. Also, we are sticking with logistic regression model for now, so changing classifier is also out of the…

Implementing logistic regression learner with python

Logistic regression is the next step from linear regression. The most real-life data have a non-linear relationship; thus, applying linear models might be ineffective. Logistic regression is capable of handling non-linear effects in prediction tasks. You can think of many different scenarios where logistic regression could be applied. There can be financial, demographic, health, weather, and other data where the model could be implemented and used to predict subsequent events on future data. For instance, you can classify emails into spam and non-spam, transactions being a fraud or not, and tumors being malignant or benign. In order to understand logistic regression, let’s cover some basics, do a simple classification on data set with two features, and then test it on real-life data with multiple features.

Building and evaluating Naive Bayes classifier with WEKA

This is a follow-up post from previous where we were calculating Naive Bayes prediction on the given data set. This time I want to demonstrate how all this can be implemented using the WEKA application. I highly recommend visiting their website and getting the latest release. WEKA is a compelling machine learning software written in Java. It is a widely-used and highly regarded machine learning software that offers a range of powerful data mining and modeling tools. It provides a user-friendly interface, making it accessible to both experienced and novice users. Weka offers a wide range of algorithms and data pre-processing techniques, making it a flexible and robust tool for various machine learning applications, such as classification, clustering, and association rule mining. You can find plenty of tutorials on youtube on how to get started with WEKA. So I won’t get into details. I’m sure you’ll be able to follow anyway.

Simple explanation of Naive Bayes classifier

Probably you’ve heard about Naive Bayes classifier, and likely used in some GUI-based classifiers like WEKA package. This is a number one algorithm used to see the initial results of classification. Sometimes surprisingly, it outperforms the other models with speed, accuracy and simplicity. Lets see how this algorithm looks and what does it do. As you may know algorithm works on Bayes theorem of probability, which allows to prediction the class of unknown data sets. Hoe you are comfortable with probability math – at least some basics.

Linear regression with multiple features

Single feature linear regression is simple. All you need is to find a function that fits training data best. It is also easy to plot data and learning curves. But in reality, regression analysis is based on multiple features. So in most cases, we cannot imagine the multidimensional space where data could be plotted. We need to rely on the methods we use. You must feel comfortable with linear algebra, where matrices and vectors are used. If previously we had one feature (temperature), now we need to introduce more of them. So we need to expand hypothesis to accept more features. From now and later on, instead of output y, we are going to use h(x) notation: As you can see, with more variables (features), we also end up with more parameters θ that has to be learned. Before we move let’s find relevant data that we could use for building machine learning algorithm. The data set Again we are going to use data set college cengage. This time we select health data set with several variables. The data (X1, X2, X3, X4, X5) are by city. X1 = death rate per 1000 residents X2 = doctor availability per 100,000…