This space is created to consult and consume documents, codes and inputs that we use in the Machine Learning course that I have taught at the National Banking and Securities Commission, at the Faculty of Sciences and at the Anahuac University, the syllabus is shown below:
- Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Classification
- Logistic Regression
- K-Nearest Neighbors
- Support Vector Machine
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Clustering
- K Means
- Hierarchical
- Association Rules
- A Priori
- Eclat
- Reinforcement Learning
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Natural Language Processing
- Deep Learning
- Redes Neuronales Artificiales
- Redes Neuronales Recurrenntes
- Dimensionalty Reduction
- PCA
- LDA
- Kernel PCA
- Temas Extra
- Selección de Modelos
- XGBoost
Each of the modules is designed to address three main points:
- Understand the idea of each algorithm
- Implement the algorithm in Python
- Implement the algorithm in R
If you have any questions you can write to me privately at lozas2605@gmail.com, in the header of the email, state the topic.
Sincerely,
Eduardo Lozas