Data scientist professional with a knack for artificial intelligence and computer vision. Ambitious, creative, and resourceful with a business mind. AI IBM Certified professional with two years of experience managing data science projects.
An overview of model explainability and interpretability fundamentals, AI applications, and biases in AI model predictions. We looked at utilizing SHAP and LIME to explain a Logistic Regression model and how to explain and interpret an ensemble model.
Learn how to perform dimensionality reduction with feature selection such as recursively eliminating features, handling highly correlated features, and more using Scikit-learn in Python.
Learn how you can perform K-Fold cross validation technique using the scikit-learn library in Python.
Learn how to perform different dimensionality reduction using feature extraction methods such as PCA, KernelPCA, Truncated SVD, and more using Scikit-learn library in Python.
Learn the importance of dropout regularization and how to apply it in PyTorch Deep learning framework in Python.
Learn how to perform logistic regression algorithm using the PyTorch deep learning framework on a customer churn example dataset in Python.
Learn how to handle one of the main data science common problems, which are imbalanced datasets, how to deal with them using SMOTE, tweaking class weights, and resampling in Python.
Build a recommender system for market basket analysis With association rule mining with the Online Retail dataset in Python.
Learn how to perform data analysis and make predictive models to predict customer churn effectively in Python using sklearn, seaborn and more.
Learn how to build a model that is able to detect fraudulent credit card transactions with high accuracy, recall and F1 score using Scikit-learn in Python.
Exploring the fake news dataset, performing data analysis such as word clouds and ngrams, and fine-tuning BERT transformer to build a fake news detector in Python using transformers library.
