In Week 0, you might have noticed that while Linear and Logistic Regression models are convenient and straightforward, they can only achieve a certain level of accuracy . This limitation underscores the need for more robust models that can handle diverse data types and enhance prediction quality ✅.
We have already learned about NumPy, pandas, and visualization libraries in Week 0 📚. If you haven't, please refer to the Week 0 content for a refresher.
This week, we will start by exploring various methods to evaluate and enhance your model's predictions 📊. After that, we'll delve into the realm of unsupervised learning 🤖, understanding the types of problems this class of algorithms can solve. Finally, we'll examine some advanced supervised learning algorithms 💹 designed to manage different data types and improve your predictive capabilities 📈.
At the core, the ML models we're applying are fundamentally mathematical models 📐. They utilize mathematical principles to analyze data, identify patterns, and make predictions. By understanding and leveraging these principles, we can create more sophisticated models that offer greater accuracy and reliability 🧠.
To truly understand algorithms, you need to grasp the mathematics behind them 📐. This deep understanding of the underlying principles will give you intuition on how even complex algorithms work. You don't need to memorize all the formulas, but having a solid comprehension of the concepts is essential 🧠.
Sharing some resources: