In this chapter, we have explored how data and algorithms come together to aid machine learning. Making sense of huge amounts of data is made possible by first pruning our data through data cleaning techniques and scaling and normalization processes. Feeding this data to specialized learning algorithms, we are able to predict the categories of unseen data based on the patterns learned by the algorithm from the data. We also discussed the basics of machine learning algorithms.
We explained supervised and unsupervised machine learning algorithms in detail with the naive bayes and k-means clustering algorithms. We also provided the implementation of these algorithms using the scikit-learn Python-based machine learning library. Finally, some important visualization techniques were discussed, as charting and plotting the condensed data helps you to better understand and make...
