1. Introduction to machine learning and data mining |
1.1. Preliminary concepts
1.2. Exploratory data analysis
1.3. Types of problems: classification, regression, clustering, anomaly detection, etc.
1.4. Types of learning: supervised, unsupervised, reinforcement, etc. |
2. Models for supervised and unsupervised classification of data |
2.1. Preliminary concepts
2.2. Main models: k-nearest neighbors, SVMs, clustering, etc. |
4. Data processing techniques |
4.1. Data preparation and standardization
4.2. Dimension reduction |
6. Statistical Quality Control |
6.1. Control graphs
6.2. Process capacity analysis
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