I offer the creation of Machine Learning and Deep Learning models, including those for image tasks (Regression, Classification) utilizing Neural Networks (MLP, ANN, CNN), and their transformation into interactive Streamlit web applications featuring graphs for straightforward data visualization. This service incorporates various techniques such as Tensors & Autograd, Dataset & DataLoader, Data Preprocessing & Augmentation, Transfer Learning, Hyperparameter Tuning (Optuna, GridSearchCV, RandomSearch), Model Evaluation (Confusion Matrix, Precision, Recall, F1-score), Cross Validation, Regularization (Dropout, BatchNorm, L2), Optimizers (SGD, Adam, RMSprop), and Loss Functions (CrossEntropy, MSE, MAE), along with models like Decision Tree, Random Forest, Gradient Boosting, and XGBoost. The development relies on tools including Python, PyTorch, Pandas, Numpy, Scikit-learn, Plotly, Seaborn, Matplotlib, Streamlit, and Tensorflow.