Feature Engineering
Learn essential feature engineering techniques for data preprocessing, including encoding, scaling, handling missing values, and addressing imbalanced datasets to improve machine learning model performance.
Full Data Science Mock Interview Featuring Kylie Ying
Comprehensive mock data science interview covering bot detection on social media, featuring behavioral questions, feature selection, model implementation, dataset creation, and technical details.
TCGA Biomarkers Identification Using Machine Learning - Complete Walkthrough
Comprehensive walkthrough on using machine learning to identify TCGA biomarkers, covering data preparation, neural network construction, and gene set enrichment analysis.
Classification Trees in Python from Start to Finish
Comprehensive guide to building and optimizing classification trees in Python, covering data preparation, model construction, pruning techniques, and visualization.
How to Create Melody Sequences to Train a Neural Network
Generate melody sequences for RNN-LSTM training: learn to create time series data, map songs to lists, and prepare inputs using one-hot encoding for neural network-based melody generation.
PyTorch Datasets, DataLoaders and Transforms - GPU Series Part 3
Explore PyTorch's data handling tools: Datasets, DataLoaders, and Transforms. Learn to prepare, visualize, and optimize data for GPU-accelerated deep learning using Fashion-MNIST dataset.
Feature Engineering is for Everyone
Explore feature engineering techniques for data visualization, automation, and machine learning. Learn to create and select features from various data types using encodings, vectorization, and date manipulation.