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Why start with Weka over another tool like the R environment or Python for applied machine learning?
In this post you will discover why Weka is the perfect platform for beginners interested in rapidly getting good at applied machine learning.
After reading this post you will know:
- Why getting started in applied machine learning is hard.
- The one most important thing to focus on when getting started in applied machine learning.
- How to make best use of Weka when getting started in applied machine learning.
Discover how to prepare data, fit models, and evaluate their predictions, all without writing a line of code in my new book, with 18 step-by-step tutorials and 3 projects with Weka.
Let’s get started.
Starting in Applied Machine Learning is Hard
When you start out in applied machine learning, there is so much to learn.
- There are the algorithms.
- There is the data.
- There is the specific problem you are working on.
- There is the mathematics behind it all.
- There is the tool you plan to use.
Often you need to learn a new programming language, like python or more esoteric languages like Matlab or R.
This does not have to be the case.
It is so much easier to learn one thing well rather than try, and possibly fail to learn a host of new things.
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Focus on Learning Just One Thing
The one thing to learn when you are starting in machine learning is how to deliver a result.
That is, given a problem, how to work through it and deliver a set of predictions or how to deliver a model that can generate predictions.
Not just predictions, but accurate predictions that can be delivered robustly and reliably, that you can put your name or your company’s name against and in which you can feel confident.
This is the most important skill to learn.
It often involves steps like:
- Defining your problem.
- Preparing your data.
- Evaluate a suite of algorithms.
- Improve your results with tuning and ensembles.
- Finalize your model and present results.
Learn the Process of Applied Machine Learning
The best tool to learn this process is the Weka machine learning workbench.
There are 3 main reasons why this is the case:
- Speed: you can work your problem fast, giving you more time to try lots of ideas.
- Focus: it is just you and your problem, the tool gets out of your way.
- Coverage: it provides lots of state of the art algorithms to choose from.
It saves you from the cruft that you can encounter with other platforms.
You do not need to spend weeks learning a new language or API, and can focus on learning how to work through problems efficiently and effectively.
You can focus on the one valuable thing you need to learn: the process of applied machine learning and delivering a result. Later, you can learn how to use more and different tools.
How to Best Use Weka
There is a specific way that you can use Weka to best aid you on your machine learning journey.
- Practice on small in-memory datasets. These are datasets with hundreds or thousands of instances so they are fast to work with and are standard datasets in the field, so that they are well understood.
- Practice on different problem types. Select standard datasets from a range of problem domains, such as biology, physics and advertising, and a range of problem types, such as binary and multi-class classification, regression, unbalanced datasets, and more.
- Practice by exercising different parts of the tool. Use a range of different techniques on different problems, including filtering methods, machine learning algorithms and even unsupervised methods like clustering and association rules.
These three simple principles will help you greatly accelerate your progress in developing skills in applied machine learning.
Your learning will be focused on working through a problem and delivering a result in the form a set of accurate and reliable predictions or a model that can make ongoing predictions.
The benefits of this approach will mean that you can greatly outpace others starting out in the field that are:
- Still figuring out how to implement an algorithm from scratch in code.
- Still figuring out how to use an esoteric programming language or application programming interface.
- Still figuring out how to setup their environment.
In applied machine learning, fast, reliable and systematic turnaround of results is more important than state of the tools. For this and more, Weka is your way forward
In this post you discovered the importance of the Weka machine learning workbench for beginners in applied machine learning.
- That getting started in applied machine learning is hard because there is so much to learn.
- That the one most important thing to focus on in applied machine learning is delivering a reliable and robust result.
- That Weka can best be used by practicing on a suite of standard machine learning datasets.
Do you have any questions about Weka for applied machine learning or about this post? Ask your questions below in the comments and I will do my best to answer.