Ever found yourself at the exciting crossroads of wanting to start a machine learning project but unsure of where to begin? If so, you’re not alone! This guide is here to help, offering insights that could be particularly beneficial for projects like the midterms or capstone projects featured in Alexey Grigorev‘s exceptional course at DataTalksClub. By the way, if you haven’t checked out Alexey’s free course (ML Zoomcamp) yet, it’s a golden opportunity to build your knowledge from the ground up and put those skills into practice.
Embark on Your Machine Learning Journey
In this article series, I aim to provide you with a practical checklist, a roadmap of crucial considerations, and a curated list of insights gathered from my own experiences. I’m no ML guru, but I’ve walked this path and want to share what I’ve learned along the way.
Refresher of CRISP-DM Process: Your Blueprint for Success
In a previous article, I introduced you to the CRISP-DM (Cross-Industry Standard Process for Data Mining) machine learning process, encompassing six critical phases:
Figure 1 – Cross-industry standard process for data mining
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
These phases act as stepping stones guiding you from defining your project goals to deploying a solution. To make things even clearer, I’ll break down each phase and assign actionable steps, helping you organize your approach systematically. Throughout the series, I’ll also sprinkle in links to valuable resources for diving deeper into specific topics.
What’s Next?
In the upcoming Part 2, we’ll begin our journey with a twist – we won’t start with Business Understanding. Intrigued? You should be! I believe there’s something else to ponder when embarking on an ML project. Join me as we unravel the mysteries of project initiation and discover the vital considerations that often fly under the radar.
Stay tuned, and let’s make your machine learning venture an exciting and fulfilling experience!
knowMLedge.com 