---

My journey to understand machine learning began a year ago, but as someone who has always been curious about the capabilities of AI, I’ve only recently made any real beginner breakthroughs. My new approach is to interactively work with various GPT models and formats to design an interactive machine learning model that learns directly through my inputs. In this blog series, I document my experiences, the challenges I faced, and the solutions I discovered along the way.

Setting the Scene

Machine learning and artificial intelligence have both always seemed intriguing and intimidating. With so many technical terms and complex algorithms, it’s easy to feel overwhelmed. I was determined to push past the initial confusion and embark on a project that would not only teach me the fundamentals of machine learning but also allow me to build something interactive and useful.

I won’t pretend to be an expert here, but one concept that particularly caught my attention was active learning. Unlike traditional machine learning models that passively receive data, active learning models actively query the user for data labels, focusing on the most informative samples to improve their performance. This approach seemed perfect for someone like me, eager to learn through hands-on interaction.

It wasn’t without its fair share of challenges and setbacks.

What to Expect in This Series

Throughout this series, I’ll share the step-by-step process of setting up my machine learning environment, building the interactive web app, and tackling the various challenges that arose. Here’s a sneak peek at what’s coming up:

1. Setting Up the Environment: I’ll walk you through the initial steps of installing libraries, generating datasets, and initializing the model.
2. Building the Interactive Web App: You’ll see how I used Flask to create a web application that interacts with the model in real-time.
3. Challenges and Solutions: From handling data dimensionality issues to ensuring accurate model evaluation, I’ll cover the hurdles I encountered and how I overcame them.
4. Visualizing Data: I’ll discuss the importance of data visualization and how it helped me understand my model better.
5. Recap of the Journey: Finally, I’ll reflect on the entire experience, sharing my learnings and future plans.

I hope you’ll join me on this exciting adventure of machine learning. Stay tuned for the next post, where I’ll take you through the initial setup process and the first steps of my project. Feel free to share your thoughts and experiences in the comments—let’s learn together!