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Weka Machine Learning Mini- Course - Machine Learning Mastery. Become A Machine Learning Practitioner in 1. Machine learning is a fascinating study, but how do you actually use it on your own problems? You may be confused as to how best prepare your data for machine learning, which algorithms to use or how to choose one model over another. In this post you will discover a 1.
Weka platform without a single mathematical equation or line of programming code. After this mini course: You will know how to work through a dataset end- to- end and deliver a set of predictions or a high- performance model. You will know your way around the Weka machine learning workbench including how to explore algorithms and design controlled experiments. You will know how to create multiple views of your problem, evaluate multiple algorithms and use statistics to choose the best performing model for your own predictive modeling problems. Let’s get started.(Tip: You might want to print or bookmark this page so that you can refer back to it later)Applied Machine Learning With Weka Mini- Course.
Photo by Leon Yaakov, some rights reserved. Who Is This Mini- Course For?
To convert.csv to.arff file format to use in Weka. Note.csv file should be proper, else it will not convert to.arff file. It should not contain any null value in. Before we get started, let’s make sure you are in the right place.
The list below provides some general guidelines as to who this course was designed for. Don’t panic if you don’t match these points exactly, you might just need to brush up in one area or another to keep up. You are a developer that knows a little machine learning.
This means you know about some of the basics of machine learning like cross validation, some algorithms and the bias- variance trade- off. It does not mean that you are a machine learning Ph. D, just that you know the landmarks or know where to look them up. This mini- course is not a textbook on machine learning. It will take you from a developer that knows a little machine learning to a developer who can use the Weka platform to work through a dataset from beginning to end and deliver a set of predictions or a high performance model. Mini- Course Overview (what to expect)This mini- course is divided into 1. Each lesson was designed to take you about 3.
You might finish some much sooner and for others you may choose to go deeper and spend more time. You can complete each part as quickly or as slowly as you like.
A comfortable schedule may be to complete one lesson per day over a two week period. Highly recommended. The topics you will cover over the next 1. 1: Download and Install Weka. 2: Load Standard Machine Learning Datasets. 3: Descriptive Stats and Visualization.
Last Updated: Dec. This dataset contains data collected through controlled, laboratory conditions. If you are in 'real world' data, please. First hebrew weka lesson - how to create an arff file from excel. نرمافزار داده کاوی Weka 1-مقدمه تا به امروز نرم افزار های تجاری و آموزشی فراوانی برای داده.
WEKA Instructions. Overview WEKA is a data mining suite that is open source and is available free of charge.
If you want to be able to change the source code for the. Need more help with Weka for Machine Learning? Take my free 14-day email course and discover how to use the platform step-by-step.
Click to sign-up and also get a. 4: Rescale Your Data. 5: Perform Feature Selection on Your Data. 6: Machine Learning Algorithms in Weka. 7: Estimate Model Performance. 8: Baseline Performance On Your Data. 9: Classification Algorithms.
0: Regression Algorithms. 1: Ensemble Algorithms. 2: Compare the Performance of Algorithms.
3: Tune Algorithm Parameters. 4: Save Your Model. This is going to be a lot of fun.
You’re going to have to do some work though, a little reading, a little tinkering in Weka. You want to get started in applied machine learning right?(Tip: All of the answers these lessons can be found on this blog, use the search feature)Any questions at all, please post in the comments below. Share your results in the comments. Hang in there, don’t give up! Need more help with Weka for Machine Learning?
Take my free 1. 4- day email course and discover how to use the platform step- by- step. Click to sign- up and also get a free PDF Ebook version of the course. Start Your FREE Mini- Course Now! 1: Download and Install Weka. The first thing to do is install the Weka software on your workstation. Weka is free open source software.
It is written in Java and can run on any platform that supports Java, including: You can download Weka as standalone software or as a version bundled with Java. If you do not already have Java installed on your system, I recommend downloading and installing a version bundled with Java. Your task for this lesson is to visit the Weka download page, download and install Weka on your workstation. 2: Load Standard Machine Learning Datasets. Now that you have Weka installed, you need to load data. Weka is designed to load data in a native format called ARFF. It is a modified CSV format that includes additional information about the types of each attribute (column).
Your Weka installation includes a subdirectory with a number of standard machine learning datasets in ARFF format ready for you to load. Weka also supports loading data from raw CSV files as well as a database and converts the data to ARFF as needed. In this lesson you will load a standard dataset in the Weka Explorer. Start Weka (click on the bird icon), this will start the Weka GUI Chooser. Click the “Explorer” button, this will open the Weka Explorer interface.
Click the “Open file” button and navigate to the data/ directory in your Weka installation and load the diabetes. Note, if you do not have a data/ directory in your Weka installation, or you cannot find it, download the. Weka from the Weka download webpage, unzip it and access the data/ directory.
You have just loaded your first dataset in Weka. Try loading some of the other datasets in the data/ directory. Try downloading a raw CSV file from the UCI Machine Learning repository and loading it in Weka.
3: Descriptive Stats and Visualization. Once you can load data in Weka, it is important to take a look at it. Weka lets you review descriptive statistics calculated from your data. It also provides visualization tools. In this lesson you will use Weka to learn more about your data.
Open the Weka GUI Chooser. Open the Weka Explorer. Load the data/diabetes. Click on different in the “Attributes” list and review the details in the “Selected attribute” pane. Click the “Visualize All” button to review all attribute distributions.
Click the “Visualize” tab and review the scatter plot matrix for all attributes. Get comfortable reviewing the details for different attributes in the “Preprocess” tab and tuning the scatter plot matrix in the “Visualize” tab. 4: Rescale Your Data. Raw data is often not suitable for modeling. Often you can improve the performance of your machine learning models by rescaling attributes.
In this lesson you will learn how to use data filters in Weka to rescale your data. You will normalize all of the attributes for a dataset, rescaling them to the consistent range of 0- to- 1. Open the Weka GUI Chooser and then the Weka Explorer. Load the data/diabetes. Click the “Choose” button in the “Filter” pane and select unsupervised.
Click the “Apply” button. Review the details for each attribute in the “Selected attribute” pane and note the change to the scale.
Explore using other data filters such as the Standardize filter. Explore configuring filters by clicking on the name of the loaded filter and changing it’s parameters. Test out saving modified datasets for later use by clicking the “Save” button on the “Preprocess” tab. 5: Perform Feature Selection on Your Data. Not all of the attributes in your dataset may be relevant to the attribute you want to predict.
You can use feature selection to identify those attributes that are most relevant to your output variable. In this lesson you will get familiar with using different feature selection methods.
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