- Models of machine learning
-
Supervised learning
- Binary classification
Validation:
- Cross validation (KFold)
- Shuffle KFold
- Stratified KFold
- GroupKFold
- Pipeline
- GridSearchCV and RandomizedSearchCV for optimization of hyper-parameters
Scikit:
- Linear Support Vector Classification (Linear SVC)
- Support Vector Classification (SVC)
- Decision tree Classsifier
- Dummy Classifier
- Multinomial Naive Bayes
- AdaBoost Classifier
Multidimensional data:
- Random Forest Classifier
- Random Forest with SelectKBest
- Evaluate results with Confusion Matrix
- Random Forest with RFE (Recursive feature elimination)
- Random Forest with RFECV (Cross-validation estimator)
- Random Forest with PCA
- Random Forest with TSNE
- Binary classification
Validation:
-
Unsupervised learning
- K-Means
- DBSCAN
- Meanshift
- Silhouette coefficient
- Davies bouldin index calculation
- Calinski Harabasz index calculation
Validation:
- Relative validation
- Cluster structure validation
- Cluster stability validation
-
Deep Learning Keras with Tensorflow - ReLu - Softmax - Export model to .h5 file PyTorch - ReLU - Perceptron - MLP (Multi-layer perceptron)
-
MLOps Create a web api to use a ml model using Flask and pickle
-
NLP
- MLE
- LaPlace
- NLTK
- Bigrams
- Language detection
- tokenizers
-
-
Notifications
You must be signed in to change notification settings - Fork 0
p-ferreira/machine-learning-studies
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
Machine learning studies
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published