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.
This article discusses the preprocessing steps of tokenization, stemming, and lemmatization in natural language processing. It explains the importance of formatting raw text data and provides examples of code in Python for each procedure.
Learn how to benefit from the encoding/decoding process of an autoencoder to extract features and also apply dimensionality reduction using Python and Keras all that by exploring the hidden values of the latent space.
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 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.
Explore different pre-trained transformer models in transformers library to paraphrase sentences in Python.
Learn how to fine-tune the current state-of-the-art EffecientNet V2 model to perform image classification on satellite data (EuroSAT) using TensorFlow in Python.