In the previous parts, we have covered types of ML, how models make predictions, and how to measure their success. Now, let’s unlock the final level: tuning and training smarter models. In this post, we will explore: These are the dials, levers, and engines behind every great machine learning model. Hyperparameter Tuning Hyperparameters are configuration …

You have built a Machine Learning model. But, how do you know if it’s actually good? In this part of the series, we will break down the core evaluation metrics used for classification models: Confusion Matrix Confusion Matrix is a table showing how many predictions your model got right or wrong. And in what way. …

Now that we have covered what machine learning models do it’s time to understand how to prepare the date they learn from. In this post, we will go through three behind-the-scenes heroes of ML success: Feature Engineering Feature Engineering means creating new features from your existing dataset to help your model understand it better. For …

In Part 1 of this series, we went through the three core types of machine learning: Supervised, Unsupervised, and Reinforcement Learning. Now, let’s zoom in on the two most common types of supervised learning: Classification and Regression. Both are used to make predictions, but they answer different kinds of questions. Classification Classification is about predicting …

Machine learning sounds complex, but it need not be. At its core, it is just about teaching computers to learn patterns from data. Irrespective of your background, this series breaks down AI/ML concepts with simple examples and real-world use cases. Welcome to the series! In the first post, we will explore the three types of …