From Biotechnology to Artificial Intelligence: My Journey into AI

My experience of changing my career path, learning AI, and entering the world of Computer Vision

From a Scientific Background to a New Path

My professional journey began in Biotechnology, a field that taught me how to approach complex problems, work with data, and pay attention to details. Over time, I became increasingly interested in technology and the potential of artificial intelligence to solve problems in different fields.

As my interest in technology grew, I decided to explore a new path in Artificial Intelligence. What initially started as curiosity gradually became a serious learning journey, leading me to Machine Learning, Deep Learning, and eventually Computer Vision.

Taking My First Steps in AI

My first serious step into this field was learning Python. After becoming familiar with programming concepts, I started learning Machine Learning and working with tools and libraries such as NumPy, Pandas, and Scikit-learn. Working on different projects helped me understand how theoretical concepts could be applied to real-world data.

I then became interested in Deep Learning and Neural Networks. Working with TensorFlow and Keras allowed me to explore different neural network architectures and gain a better understanding of how models learn from data. At this stage, I realized that the best way for me to learn was through practical projects and experimentation.

Why Computer Vision?

Among the different areas of Artificial Intelligence, Computer Vision became particularly interesting to me. The ability of a computer system to analyze images and videos, detect objects, and extract useful information from visual data fascinated me.

This interest encouraged me to learn more about image processing and deep learning models for Computer Vision. From image classification to object detection and video analysis, each project gave me an opportunity to develop my skills and gain more practical experience.

Working on Real-World Projects

One of the projects that gave me valuable practical experience was a pill detection, tracking, and color classification system. In this project, I used YOLO-based models to detect pills and tracking techniques to follow them across different video frames. Working on this project allowed me to experience different stages of a Computer Vision system, from model training to video inference and result analysis.

Projects like this taught me that building an AI system is not simply about choosing a model and training it. Data preparation, model evaluation, debugging, optimization, and finding the right solution for real-world conditions are all important parts of the process.

Combining Biotechnology and Artificial Intelligence

My background in Biotechnology remains an important part of my professional journey. I believe that combining knowledge from life sciences with Artificial Intelligence can create interesting opportunities for working on problems related to biology, healthcare, and biological data analysis.

At the same time, learning AI has allowed me to look at scientific problems from a different perspective. My goal is to eventually combine knowledge from both fields and develop practical solutions where Artificial Intelligence can work alongside biological sciences.

My Journey Continues

Learning Artificial Intelligence is not a destination for me; it is an ongoing journey. My current focus is on Machine Learning, Deep Learning, and especially Computer Vision, and I continue to improve my skills by studying new concepts and working on practical projects.

This article is part of my personal AI journey. Visit my personal website to explore my projects, experience, and other work in Artificial Intelligence and Computer Vision.