My Introduction to Artificial Intelligence and Machine Learning
A few weeks ago, I decided to start learning about Artificial Intelligence (AI) and Machine Learning (ML). I had heard these terms thrown around in tech circles, but I never really understood what they meant and why they mattered so much. So I decided to take the plunge and start from the very beginning.
What is Artificial Intelligence?
At its core, Artificial Intelligence is about making machines smart — enabling them to perform tasks that typically require human intelligence. This includes things like:
- Understanding language (like chatbots)
- Recognizing faces in photos
- Making decisions based on data
- Playing games or driving cars
What surprised me is that AI isn't just one single thing — it's an umbrella term that covers many technologies and approaches.
Where Machine Learning Fits In?
Machine Learning is actually a subset of AI. It’s about teaching machines to learn from data instead of being explicitly programmed for every single task. That’s what makes it so powerful.
Machine Learning lets a computer improve at a task — like predicting house prices — just by being exposed to more examples, without being told the exact formula.
Why This Interests Me
The reason I find this space so exciting is because it blends logic, data, and creativity. It’s not just about coding — it’s about solving real problems. From Netflix recommendations to fraud detection in banks, ML is quietly powering a lot of the digital world we interact with every day.
Where I Go From Here
I want to dive deeper into the types of Machine Learning — supervised, unsupervised, and reinforcement learning. I also plan to explore popular algorithms and tools like scikit-learn and TensorFlow. More importantly, I want to start experimenting with small projects that apply what I learn — even something simple like predicting exam scores from past data.
