Microcredential in Applied Machine Learning

The ongoing digital transformation is changing the way companies work and evolve. Improvements in the automatic acquisition of data from company operations have made automatic techniques for processing large volumes of information even more relevant. This micro-module aims to provide knowledge of the most common automatic learning techniques used by machines and best practices in designing solutions, measuring their performance, and understanding their potential and limitations.

Contents:

1. Learning Types
1.1. Supervised
1.2. Unsupervised
1.3. Semi-supervised
2. Learning Algorithms
2.1. Linear Regression
2.2. Naive Bayes
2.3. Decision Trees
2.4. kNN
2.5. SVM
2.6. Neural Networks
3. Model Performance Measurement and Parameter Fine-Tuning
3.1. Methods Holdout, K-Fold and Leave-One-Out
3.2. Confusion Matrix
3.3. Pearson’s Correlation