Important note Of course, grid search cannot guarantee that you will come up with your target performance. That depends on the algorithm and the training data. A common practice, though, is to define the values for testing by using a linear space or log space, where you can manually set the limits of the hyperparameter […]

Model optimization As you know, understanding evaluation metrics is very important in order to measure your model’s performance and document your work. In the same way, when you want to optimize your current models, evaluating metrics also plays a very important role in defining the baseline performance that you want to challenge. The process of […]

Summarizing precision and recall Sometimes, you might want to use a metric that summarizes precision and recall, instead of prioritizing one over the other. Two very popular metrics can be used to summarize precision and recall: F1 score and Area Under Curve (AUC). The F1 score, also known as the F-measure, computes the harmonic mean […]

Extracting metrics from a confusion matrix The simplest metric that can be extracted from a confusion matrix is known as accuracy. Accuracy is given by the following equation, as shown in Figure 7.2: Figure 7.2 – Formula for accuracy For the sake of demonstration, Figure 7.3 shows a confusion matrix with data. Figure 7.3 – […]

It is now time to learn how to evaluate and optimize machine learning models. During the process of modeling, or even after model completion, you might want to understand how your model is performing. Each type of model has its own set of metrics that can be used to evaluate performance, and that is what […]

Exam Readiness Drill – Chapter Review Questions Apart from a solid understanding of key concepts, being able to think quickly under time pressure is a skill that will help you ace your certification exam. That is why working on these skills early on in your learning journey is key. Chapter review questions are designed to […]

Image processing Image processing is a very popular topic in machine learning. The idea is pretty self-explanatory: creating models that can analyze images and make inferences on top of them. By inference, you can understand this as detecting objects in an image, classifying images, and so on. AWS offers a set of built-in algorithms you […]

Textual analysis Modern applications use Natural Language Processing (NLP) for several purposes, such as text translation, document classifications, web search, Named Entity Recognition (NER), and many others. AWS offers a suite of algorithms for most NLP use cases. In the next few subsections, you will have a look at these built-in algorithms for textual analysis. […]

Dimensionality reduction Another unsupervised algorithm that was implemented by AWS in its list of built-in algorithms is known as principal component analysis, or PCA for short. PCA is a technique that’s used to reduce the number of variables/dimensions in a dataset. The main idea behind PCA is plotting the data points to another set of […]

Conclusion That was a really good accomplishment: you just mastered the basics of clustering algorithms and you should now be able to drive your own projects and research about this topic! For the exam, remember that clustering belongs to the unsupervised field of machine learning, so there is no need to have labeled data. Also, […]