Cursos de Aprendizaje Automático

4 horas

32 capítulos

Intermedio

1 ESTUDIANDO AHORA

Habilidades Adquiridas: Machine Learning with scikit-learn, Model Training and Evaluation, Hyperparameter Tuning

2 horas

19 capítulos

Intermedio

Habilidades Adquiridas: Linear Regression with Python, Model Training and Evaluation

3 horas

24 capítulos

Intermedio

Habilidades Adquiridas: Python Programming, Python Classification Models, Logistic Regression, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning

4 horas

34 capítulos

Intermedio

Habilidades Adquiridas: Clustering fundamentals and algorithms , Handling missing and categorical data , Data normalization and distance metrics , K-Means: principles and cluster optimization , Hierarchical clustering and dendrograms , DBSCAN: noise handling and irregular shapes , Gaussian Mixture Models: probabilistic clustering

6 horas

37 capítulos

Avanzado

Habilidades Adquiridas: Reinforcement Learning Foundations, Multi-Armed Bandit Algorithms, Dynamic Programming Methods, Monte Carlo Techniques, Temporal-Difference Learning, Gymnasium Basics

1 hora

16 capítulos

Principiante

Habilidades Adquiridas: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution

1 hora

12 capítulos

Principiante

Habilidades Adquiridas: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building

2 horas

16 capítulos

Intermedio

Habilidades Adquiridas: Classification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) , Regression metrics (MSE, RMSE, MAE, R²) , Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) , Dimensionality reduction evaluation , Anomaly detection evaluation , Cross-validation techniques

1 hora

12 capítulos

Principiante

1 ESTUDIANDO AHORA

Habilidades Adquiridas: Overfitting and Regularization, L1, L2, and Elastic Net Regularization, Feature Selection Methods, Pipeline Construction, Hyperparameter Tuning, Coefficient Visualization

1 hora

11 capítulos

Principiante

Habilidades Adquiridas: Hyperparameter Tuning Fundamentals, Manual Search Methods, Automated Search with scikit-learn, Bayesian Optimization, Model Evaluation and Generalization

1 hora

16 capítulos

Principiante

1 ESTUDIANDO AHORA

Habilidades Adquiridas: MLOps Fundamentals, Experiment Tracking with MLflow, Model Deployment with FastAPI and Docker, Pipeline Automation with Airflow, Model Monitoring and CI/CD

5 horas

51 capítulos

Principiante

Habilidades Adquiridas: Functions & Sets, Series Analysis , Limits & Derivatives , Integrals , Gradient Descent , Vectors & Matrices , Linear Transformations , Matrix Decomposition , Probability Rules , Bayes' Theorem, Statistical Measures , Probability Distributions