Course program:
1. Linear regression with a variableIn statistics and mathematics, a "variable" is a symbol that represents a value that can change or vary. There are different types of variables, and qualitative, that describe non-numerical characteristics, and quantitative, representing numerical quantities. Variables are fundamental in experiments and studies, since they allow the analysis of relationships and patterns between different elements, facilitating the understanding of complex phenomena....
2. Linear Algebra Review
3. Linear regression with multiple variables
4. Octave tutorial
5. Logistic regression
6. RegularizationRegularization is an administrative process that seeks to formalize the situation of people or entities that operate outside the legal framework. This procedure is essential to guarantee rights and duties, as well as to promote social and economic inclusion. In many countries, Regularization is applied in migratory contexts, labor and tax, allowing those who are in irregular situations to access benefits and protect themselves from possible sanctions....
7. Neural networks: representation
8. Neural networks: learning
9. Tips for applying machine learning
10. Design of machine learning systems
11. Support Vector Machines
12. Unsupervised learningUnsupervised learning is a machine learning technique that allows models to identify patterns and structures in data without predefined labels. Through algorithms such as k-means and principal component analysis, This approach is used in a variety of applications, such as customer segmentation, anomaly detection and data compression. Its ability to reveal hidden information makes it a valuable tool in the...
13. Decreased dimensionality
14. Anomaly detection
15. Recommendation systems
16. Large-scale machine learning
17. Application example: Photo OCR
Duration:
10 weeks
Important date:
Contact institute
