Learn how to build machine learning and deep learning models for many purposes in Python using popular frameworks such as TensorFlow, PyTorch, Keras and OpenCV.
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 how you can perform named entity recognition using HuggingFace Transformers and spaCy libraries 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.