Experimenting with machines and making them more human is what the technical advancements all about, where the collaborative efforts of both man and machines are deriving results beyond expectations. Next, to the invention of Microchip, Artificial Intelligence(AI) and Machine Learning(ML) are considered to be the biggest innovation of technology. From a fanciful concept of science fiction, AI is now the reality of this digital world. With AI came ML and imitating the real neurons, Deep Learning brings the study of neural networks, giving machine learning a great breakthrough. It’s the era of digital revolutions where the focus is merely to harness mental and cognitive abilities. And the day is not far when automated devices and programs will not only replace ‘manual labor’ but also the ‘mental labor’ which only a human performs today. People looking at such advancements raise their brows in surprise and imagine how these technologies work and run. I would say that technology lovers seeking to gain deeper insights about the latest advancements must undergo a Machine Learning Course which will drive you away with the digital benefits they provide to the world.
Some sixty years ago, artificial intelligence was just a concept that research scientists had in mind. But ever since the idea of super-computers-capable-of-thinking-like-humans has been floated, it has occupied a particular part in the public consciousness. Over recent years, we have seen tremendous growth and rapid evolution of artificial intelligence. Today, there is a vast amount of high-quality open source libraries and software tools available to AI and ML experts. Every day, new ideas and concepts on AI are being discovered, as well as new applications of AI are being explored. We see how AI is slowly being used in business and our everyday lives. According to Ottawa IT services experts from Firewall Technical, AI technology will continue to be a significant force in many IT solutions in the next few years. Many tech experts agree that AI has a very bright future ahead and some even predict the drastic changes AI can bring into the future generations. Considering all these great news now is the best time to become an AI master. But for you to become an AI expert, you’ll need to learn some useful tools in building AI algorithms.
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 make 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 important 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.
Previously we have tried to run weka server to utilize all cores of the processor in classification tasks. But it appears that weka server works only in explorer for classification routines. For more advanced machine learning there is a more flexible tool – experimenter. Weka server doesn’s support this area. So what to do if you want more performance or utilize 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 experiment on Weka wikispaces, but in some cases, 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.
Currently, WEKA is one of the most favorites machine learning tools. Without programming skills, you can do serious classification, clustering, and big data analysis. For some time I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since researches 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, the utilize a single core of your processor. Such algorithms as Multi-layer Percepron 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 fold of operation. 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…
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 lots of 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 next events on future data. For instance, you can classify emails into spam and non-spam, transactions being fraud or not, 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.
Artificial Intelligence (AI) is the field of computer science which uses mechanical and computational processes to echo almost all aspects of human intelligence. AI can perform multiple functions: sensory interaction with the environment and the ability to make decisions about events that haven’t happened yet without any human assistance whatsoever. Targeted advertising and virtual agents that recognize the patterns of your behavior are much standards in today’s online undertakings. Artificial Intelligence is used by business enterprises in data analysis algorithms which have the highest advantage of analyzing the Big Data, and it also involves customer engaging techniques. Apart from IBM which developed some of the earliest functions of AI, Google and Facebook are also using AI for the analytic purpose of the massive amount of data they receive.