Machine learning (ML) is a part of artificial intelligence that teaches the computer to work and making decision based on historical data. It is a collection of algorithms that apply computational methods to learn the information from given dataset, then come out with prediction model in term of mathematical model or equation. This model able to perform predictions or deduce patterns from the supply dataset.
To work with ML, a data scientist should requires a good fundamental knowledge in Mathematics and Statistics, capability to process data and interpret the results. To process the data, you have to use a specific tools or program. Thus one of the requirement of data scientist to work with machine learning is to have a programming skill, so they can build their own program to process and train the data.
The most common way of developing Machine Learning program is by writing a series of coding using programming language such as Python and R. For someone with education background in computer science and have experience in programming, it will be an advantage for them. There are many benefits of using traditional programming method, such as easy customization, availability of open-source tools, and vast amount of support and forum discussing about via the internet.
But for someone who just started to explore the field Data Science and require to develop a ML program with no programming experience, they may experience some difficulties of writing the codes based on selected algorithm. It may requires a lots of time to mastering programming language, such as Python and apply it to develop ML algorithm. Thus, this article is purposely written for those who want to generate the machine learning model without coding activities. Instead, they will use alternative programming method, which is called as visual programming.
Visual Programming is a programming technique that allow users to construct the programming algorithm using functional graphical element or widget, without writing the programming text script. Each widget has their own specific function. In another word, each widget resemble the part of programming script to execute specific task that require in the process. A complete set of fully functional program is comprises of multiple widgets with different function that interconnected to each other in logic sequence, which is called as workflow.
KNIME Analytics Platform is an open-source and free visual programming tools with integrated environment for data science and machine learning task. The full version of the software is completely free with no limitation of usage. It capable to perform:
Data exploration - statistical analysis, correlation analysis.
Data preparation - data cleaning, handling categorical variable, data normalization, partitioning.
Data processing - training machine learning algorithm, apply machine learning model.
Model validation - Performance evaluation using R-Squared and confusion matrix.
Visualization - Heat map, scattered plot.
What traditional programming can do, also can be done with KNIME platform. Both method can deliver the same results. What make it difference is process of achieving the goal. Traditional programming require user to type a computer code to process the data using programming language such as Python. While, KNIME utilize interactive visual approach using drag and drop nodes that build-up sequence of instruction to work with the data.
The functional widget in KNIME is called node. Node has been pre-programmed and can be configured based on user's requirement. Each node has their own function and interconnected to each other to process the data. A group of interconnected nodes is called workflow.
The workflow of nodes should be logic sequence (this rule also applied in traditional programming), so that no error occurred while executing the node and the right result can be obtained. To use KNIME for build a machine learning model, the user should have understanding the complete flow of machine learning, from data preparation until model validation.
It is a complete solution and alternative for non-programmer to work with machine learning and data science. Learn to write a code may not be easy for some people, and more time require to fully master the specific language. Some business problems are complex and require sophisticated programming process, which is not easy for newbie.
With KNIME, you can save lots of time of being a real data scientist. Instead of taking too much time of learning the tools, it is more important if you can spend more time for interpreting the results of your model and analytics, the main task as a data scientist.
This is very suitable to be used for professional who in the field of finance, banking, cybersecurity, marketing, sales, education, engineering and etc. I personally do professional coaching on data science and machine learning with KNIME Analytics Platform (For more info click into this link) for corporate organization and public. I conduct in house-training, in-class training and webinar training. Watch my video here for more information.
I have made a simple video of using KNIME to build machine learning model for linear regression problem. The video will show you step-by-step guidelines, from importing dataset up to validation of the model. Feel free to contact me if you have any enquiries about topic related with data science, machine learning and KNIME at Contact Me page in my website or by commenting at the comment section.
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