Last Updated on
How can you learn about a machine learning tool quickly?
Using the right tool can mean the difference between getting good predictions quickly and a project on which you cannot deliver. You need to evaluate machine learning tools before you use them.
You need to know that a machine learning tool is right for you, right for your project and that you can trust it.
In this post you will discover how you can rapidly design and fill in a one-page algorithm description template. You can use machine learning tool templates like this to evaluate a machine learning tool and directly compare and contrast it to other tools.
Questions About Machine Learning Tools
It is difficult to discover whether a machine learning tool is right for you.
The selection and adoption of a machine learning tool for a project is an important decision. It must provide the capabilities you need to meet the objectives of your project. It must also provide the interfaces, documentation, support and everything that you need to actually use it in practice.
Sometimes it is critical to know how the tool works or what the limitations of the tool happen to be. You always need to know that you can trust the tool to deliver on it’s capabilities and that there is sufficient support available when you need it.
You have questions about a machine learning tool and it can be hard, sometimes ridiculously so, to find the answers. Worse than that, once you get the answers, you don’t have a clear way to evaluate everything you learned about the tool or a context to compare it directly to other tools.
Design And Fill-In Tool Descriptions
The answer is to record all the specific questions you have about a machine learning tool.
Take a moment and think of all the questions, that if answered, will allow you to decide whether or not a tool is right for you and whether it was a better fit or (not compared) to other tools you are considering.
You can use those questions to create a small one-page tool description template. Researching each question, you can quickly fill in that template and have a highly tailored and customized description of the tool.
It provides a structured way to both research and capture the information you need to know about the tool, as well as provide a point of evaluation and reference for comparing to other tools.
You can also store it, update it, share it and use it again and again on future projects.
Describe Any Machine Learning Tool
Using a systematic process you can describe any machine learning tool.
Quick 5-step Process
Below is a quick 5-step process to describe any machine learning tool.
- Select Tool: Select the tool that you want to describe. This may come from a short list of tools that you have previously created. Alternatively, it may be a new tool or a tool that has caught your attention that you want to know more about.
- Identify Questions: List out the questions that you have about the tool. Specific questions are useful (such as what is the license agreement?). More likely will be open ended questions that will require a summary of information (such as what tasks of the applied machine learning process does the tool cover?).
- Create Template: Take the questions and lay them out in a new text document or spreadsheet with space around each so that you can fill in answers. This is your machine learning tool template. You may like to save the uncompleted template and re-use it on future projects.
- Research: Use your favorite search engine and research your tool. Focus on one question at a time and use the language in the question as search criteria. Try to spend only a few minutes on each question capturing the high-level or broad strokes of the answer.
- Complete Template: Use your search results to fill in the template. Use bullet points and focus on the salient details that are useful and meaningful to the question and to you. Do not copy paste in chunks of text. This will not help you better understand the tool.
Tips For Great Tool Descriptions
Below are 8 tips to help you make excellent machine learning tool descriptions.
- Complete the template fast. Do not spend more than 30-to-60 minutes creating your template. The tool and the template are not the project. Complete it fast, capture the broad brush strokes and use the template. You can always come back and perform another round of research.
- Make your template targeted. Do not try to capture all the attributes of a machine learning tool. Focus on the 5-to-10 questions you really need answered about a tool before you can make a decision as to whether it will be useful to you. These questions will very likely focus around the capabilities of the tool and your trust that the tool can deliver.
- Adopt a consistent template. Consider using the same template when evaluating different tools. Using the same structure will make direct comparison and contrasting to other tools a whole lot easier.
- Use a spreadsheet. If you don’t like writing, consider using a spreadsheet. List attributes as column headings and use each row as a new dot point under the heading. It will force you to be concise.
- Share your results. Share your results with friends, colleagues or publicly. Chances are if you were interested enough in a tool to evaluate it, then it is likely that other people will be and may benefit from your description.
- Build upon your results. You can re-visit a tool description in the future and update it. This can be useful if the tool changes on a new release or you are about to begin a new project.
- Reach out for more detailed information. If there are questions that you cannot get clear answers to, consider reaching out to users and even developers of the tool via email or by posting forum messages. Clear and direct questions would be the most efficient approach.
- Describe using bullet points. This is a great approach in general and especially if you do not like writing. It keeps the description clear, targeted and useful.
Example Questions For Template
Below are 10 sample questions that you can use to build your machine learning tool template.
- What is the full name, nicknames and acronyms for the tool?
- What is the license agreement for the tool?
- What programming languages can be used with the tool?
- What interfaces are provided for the tool (e.g. graphical, command line, programming, etc.)?
- What community is there around the tool (e.g. forums, plug-ins, blogs, etc.)
- Who created the tool, when and why?
- How often is the tool updated and when was the last release (e.g. recent release schedule)?
- What tasks in the applied machine learning process does the tool cover?
- What modeling algorithms does the tool provide?
- What are the key resources that can be used to master the tool (e.g. books, papers, websites)?
You Can Describe Machine Learning Tools
Your description does not need to be complete. You only need to cover the details about the tool that interest you and help you make a decision on whether it will be useful for your needs. Do not create exhaustive descriptions of tools, it would be a great waste of time for you. The tool is not the project.
You do not need to be an expert in machine learning. You do not need to know what all the algorithms are or what all the terminology means. You only need to be able to describe the attributes of the tool that matter to you to be able to make a decision.
You do not need to be an expert in the tool. Creating a description of a tool neither requires you to be an expert in the tool nor will it make you an expert in the tool. It is a process that you can use to quickly learn about the tool. You can gather all of the information you need from websites, books, papers, blogs and so on.
You do not need to be a programmer. Many machine learning tools are libraries that require you to be a programmer to use them. But there are also many machine learning tools that provide graphical, web and command line interfaces. If you are not a programmer, focus on describing tools that you can use without writing a line of code.
You do not need to be a writer. Your description of the tool does not need to be long, nor does it need to be well written. You can capture the information you need using bullet points. You can even use a spreadsheet to create the description.
In this post you discovered how you can very quickly create a description of a machine learning tool.
Machine learning descriptions can be used to evaluate a machine learning tool and compare and contrast it to other machine learning tools. It is an invaluable aid to help you decide whether a given machine learning tool is suitable for your needs or for your project.
You can describe any machine learning tool quickly using the following 5-step process:
- Select the tool you want to describe.
- Identify the attributes of the tool you want to capture.
- Layout the tool attributes in a text document as a template for you to complete.
- Research the tool online using books, papers, websites, forums and any other sources you find useful.
- Use the search results to fill in the template using bullet points.
Your Next Step
Describe a machine learning tool Right Now!
- Select a machine learning tool that you want to describe.
- Use the process above to describe it.
- Report back, I’d love to see your description.
Do you have any questions about this process? Email me or leave a comment.