Corsi di Apprendimento Automatico

4 ore

32 capitoli

Intermedio

20 STUDIANDO ORA

Competenze acquisite: Machine Learning with scikit-learn, Model Training and Evaluation, Hyperparameter Tuning

2 ore

19 capitoli

Intermedio

2 STUDIANDO ORA

Competenze acquisite: Linear Regression with Python, Model Training and Evaluation

3 ore

24 capitoli

Intermedio

2 STUDIANDO ORA

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

4 ore

34 capitoli

Intermedio

4 STUDIANDO ORA

Competenze acquisite: 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

5 ore

51 capitoli

Principiante

12 STUDIANDO ORA

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

6 ore

37 capitoli

Avanzato

1 STUDIANDO ORA

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

1 ora

16 capitoli

Principiante

1 STUDIANDO ORA

Competenze acquisite: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution

1 ora

12 capitoli

Principiante

4 STUDIANDO ORA

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

2 ore

12 capitoli

Intermedio

Competenze acquisite: Dimensionality reduction , Principal component analysis (PCA) , Covariance and eigen decomposition

2 ore

16 capitoli

Intermedio

Competenze acquisite: 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

2 ore

9 capitoli

Avanzato

Competenze acquisite: Drift Detection Fundamentals, Statistical Drift Metrics, Kolmogorov–Smirnov Test, Population Stability Index, Model-Based Drift Detection, Monitoring Model Degradation

2 ore

19 capitoli

Principiante

Competenze acquisite: Feature Scaling, Mean-Centering, Standardization, Normalization (L1, L2, Max), Whitening and Decorrelation, Preprocessing Pipelines, Data Leakage Prevention