In this course I learned a lot of concepts about machine learning and deep learning, such as:
- Pandas
- NumPy
- MatPlotlib
- Scikit-Learn
- Splitting into training, validation and test set
- Cleaning, transforming and reducing dataset
- Working with categorical classification ( OneHotEncoder )
- Handling missing values
- Regression, Classification, Decision trees and others machine learning algorithms
- Making predictions and evaluating models with score, cross validation, accuracy, ROC Curve, confusion matrix, classification report, MAE, MSE
- Tuning Hyperparameters with GridSearch and RandomizedSearchCV
- Saving and Loading model.
- Deep Learning with tensorflow
- Turning data Labels into Numbers
- Preprocess images and turning data into batches
- Building a deep learning model using some architectures such as Mobilenet.
- Handling overfitting and underfitting.
- Evaluating the model