Machine Learning Libraries Projects
ML libraries useful for data engineering tasks.
3 projects available
How to Choose the Right Machine Learning Library for Python?
To decide among the three popular machine learning libraries: Opt for Scikit-learn for traditional machine learning algorithms, especially when working with structured data and when simplicity, ease of use, and quick model development are priorities. Choose TensorFlow for deep learning projects that may need to scale to large datasets or require deployment on various platforms, well-suited for complex neural network architectures. Prefer PyTorch for dynamic neural network implementations and when ease of use, readability, and flexibility are important, particularly favored in the research community for developing custom neural network architectures.
Customer Churn Prediction with Scikit-learn
intermediateBuild a machine learning model to predict customer churn using Scikit-learn's Random Forest classifier. Learn data preprocessing, model training, evaluation metrics, cross-validation, and feature importance analysis - foundational ML skills every data engineer should master.
Energy Consumption Forecasting with TensorFlow
advancedCreate a deep learning time series forecasting model using TensorFlow and LSTM networks. Learn to build windowed datasets, train neural networks with Keras API, make future predictions, and deploy models - essential for any time-series prediction task.
Network Anomaly Detection with PyTorch
advancedImplement an autoencoder neural network in PyTorch for unsupervised anomaly detection in network traffic. Learn PyTorch's nn.Module, custom datasets, DataLoaders, and how to identify outliers - critical for security and monitoring applications.