If you do the above correctly, the Emulator app will be successfully installed. Now click Next to accept the license agreement.įollow the on screen directives in order to install the application properly. Once you have found it, click it to install the application or exe on your PC or Mac computer. Now that you have downloaded the emulator of your choice, go to the Downloads folder on your computer to locate the emulator or Bluestacks application. Step 2: Install the emulator on your PC or Mac You can download the Bluestacks Pc or Mac software Here >. Most of the tutorials on the web recommends the Bluestacks app and I might be tempted to recommend it too, because you are more likely to easily find solutions online if you have trouble using the Bluestacks application on your computer. If you want to use the application on your computer, first visit the Mac store or Windows AppStore and search for either the Bluestacks app or the Nox App >. It makes it possible to train any Weka classifier in Spark, for example.Step 1: Download an Android emulator for PC and Mac Conversely, Python toolkits such as scikit-learn can be used from Weka.įor running Weka-based algorithms on truly large datasets, the distributed Weka for Spark package is available. Weka's functionality can be accessed from Python using the Python Weka Wrapper. Weka models can be used, built, and evaluated in R by using the RWeka package for R conversely, R algorithms and visualization tools can be invoked from Weka using the RPlugin package for Weka. WEKA can be integrated with the most popular data science tools. Deep neural networks, including convolutional networks and recurrent networks, can be trained directly from Weka's graphical user interfaces, providing state-of-the-art methods for tasks such as image and text classification. WekaDeeplearning4j is a deep learning package for Weka. Note that programmers can also easily implement this pipeline using Weka's Java API: Second, we select a learning algorithm to use, e.g., the J48 classifier, which learns decision trees.įinally, we run a 10-fold cross-validation evaluation and obtain an estimate of predictive performance. Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without having to write a single line of code:įirst, we open the dataset that we would like to evaluate. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

Install weka windows