Join Keith McCormick for an in-depth discussion in this video Who is this course for?, part of Introduction to Machine Learning with KNIME.
- So, who should consider learning KNIME? If you're a student of data science and predictive analytics, you'll need a tool that's easy enough to use that you can focus on practicing data exploration and predictive modeling without getting stuck. So if you're trying to teach yourself regression, decision trees, clustering ensembles, or almost anything else, you can start in KNIME. Another group are practitioners that need an easy to use, open-source option because their workplaces are still sorting out what tools to use. My advice is, you don't need to wait for some official decision. Start practicing and start prototyping. Also, team leaders that have a diverse team. It can actually be a powerful way of combining the contributions of multiple team members, even if some use R, some use Python, and others are rookies and haven't really mastered a tool yet. Finally, you might be someone like me. As an active consultant and seminar leader, there's a problem that I run into all the time. Someone wants help on a project but they don't have access to a tool yet. Or I'm asked to do a seminar for folks from a variety of organizations and industries and I don't want to just use slides. I might want to actually show them how it's done. Since KNIME is free and easy to use, it's a great choice for data science instruction. So what should you know to fully enjoy the course? To appreciate KNIME, you'll have to have a big picture sense of what predictive modeling is all about. So, if you're brand new to the field, you might want to check out my course, Essential Elements of Data Mining and Predictive Analytics. This would be especially helpful if you're never heard of CRISP-DM, the cross-industry standard process of data mining, because I'm going to mention it from time to time in the course. Also, if you're truly starting from scratch and you've never built a predictive model before, you'll probably want to check out at least a little bit of theory. My course on decision trees in the library would be a good choice. We're going to keep it pretty basic and fast-moving, however, so you could probably watch a decision tree course after this if you choose. Okay, just a couple more things that you should know and have set up before you begin. You're going to want to install KNIME. I've chosen not to include a KNIME install lesson because it's very straightforward. Just go to knime.com and you'll be able to find good support for multiple platforms, including video support. Okay, now, there's going to be one lesson where we're going to be using R, so I'm going to encourage you to install RStudio and that will allow you to do that lesson. What we're going to be discovering is that you can use R code right within KNIME. One final note, don't worry about the KNIME extensions just yet. I'm going to be walking you through that process. So let's get started with the course.
- Why use a workbench
- Why choose KNIME?
- Adding KNIME nodes with extensions
- Accessing data
- Exploring data statistically and visually
- Merging and aggregating data in KNIME
- Modeling in KNIME
- Scoring new data
- Combining KNIME with R and Python