ML Fundamentals

AI technology encompasses four fascinating categories: numeric predictions, classifications, robotic navigation, and language processing. I had a pivotal moment when my project evolved into a groundbreaking machine-learning political tool. After investing time and effort in math and data, I was amazed by the results. Machine learning involves training massive amounts of data to make predictions rather than using traditional custom algorithms. This process leverages structured (well-organized and meaningful) and unstructured (new and unconventional) data.

Supervised learning involved manual input on various topics, DM roles, industries, and storylines, while unsupervised learning quietly worked behind the scenes to uncover hidden connections. My AI model grew through careful assessment of input weight from different topics such as AC, engineering, COVID-19, restrictions, and shots. These models were tailored to break down the importance of each topic in specific situations, differentiating between the impacts of catching COVID, getting vaccinated, and potential side effects.

Computer learning's magic lies in its ability to respond to new, credible information by adjusting its internal parameters. This continuous fine-tuning, including adjusting biases, leads to increasingly accurate results. The process of connecting weights and biases demonstrates the power of neural networks, which are built on mature data. Deep learning takes it a step further by unearthing hidden layers of meaning within the data. By carefully choosing nodes and layers and diligently working through the math, I was able to steer my project in the right direction.

I aimed to uncover the best weights and biases to generate reliable estimates and present viable solutions to the public. I'm excited to continue pushing the boundaries of what AI can achieve for the betterment of society.