Data everywhere
Thinking back to household smart devices, you might wonder: How does a streaming app know what to recommend to you? How does my robot cleaner know where to clean? Well, data plays a crucial role. These devices rely on data to operate efficiently. Data is collected to make your experience more personalized and automated.
Data is information that is collected, stored, and used for various purposes. It can come in many forms, such as text, images, and numbers. AI utilizes data for learning and reasoning. For example, when you consistently watch shows of the same genre on a streaming app, this data helps the AI model determine what to recommend to you next.
In the first video, you'll gain an understanding of what data is and why it's essential for advancing AI.
Data allows AI to learn, adapt, and make informed decisions. However, it isn't just about the quantity of data available but also the quality. The effectiveness of AI models is directly tied to the quality of the training data.
In the next video, you'll discover why finding patterns in data is important and how it can enhance user experience.
AI models use data to identify patterns. At the core of this process are algorithms that analyze data fields, learning from the patterns within the data to generate models. These models are then used to make predictions or decisions about new data. This process is called machine learning.
The quality and quantity of the data are crucial. High-quality data ensures that the AI model can learn accurately and make reliable predictions. Poor-quality data can lead to incorrect outcomes. Therefore, data preprocessing, which includes cleaning and organizing data, is an essential step in the machine learning process.