WHAT? (What did we learn?)

What is Artificial Intelligence?

      It is the machine that can learn from the data that you gave to them. So, it's up to your bias.

Type of Learning

      - Supervised - is when the model is getting trained on a labeled dataset (both input and output)

      - Unsupervised - analyzes and clusters unlabeled datasets(only output) using machine learning algorithms from hidden patterns.

      - Semi-supervised - is work between Supervised and Unsupervised when we have both labeled and unlabeled datasets.

      - Reinforcement - is model keeps on increasing its performance using Reward Feedback to learn the behavior or pattern.

Type of Machine Learning by Data Type

      - Structural Data


      - Time Series Data


      - Natural Languages


      - Image

Example of Machine Learning

      - Netflix Suggestion

      - Falcon 9 Landing

      - Tesla Full Self-Driving

      - Midjourney Image Generator

Machine Learning Tools

      - Python, Scala, Java, R language

      - Pandas

      - Sklearn

      - Tensorflow Keras

      - Yolo

      - Kaggle

      - Papers With Code

      - Teachable Machine

SO WHAT? (Why is this important?)

      This class teaches me basic AI. So, if we are interested in AI we can learn more about this later. At the same time if we didn't interested in this we will know the basics of them and I think it will help you to do some future projects.

NOW WHAT? (implications / reflections)

      This class let me know the program that can use to make AI. And how to use a Teachable Machine website that can use to make very basic machine learning because this website the AI will know only the data that you give to them but if we give too much data to them your computer will work very hard and maybe it can't finish it.