One Hot Encoding Courses and Certifications

Working with Categorical Data in Python

36 ratings at DataCamp

Learn how to manipulate and visualize categorical data using pandas and seaborn.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Introduction to Feature Engineering in R

Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

PredictHealth's Multi-Factor Cost Models

Master advanced regression techniques to build sophisticated multi-factor prediction models with data cleaning, validation, and custom feature engineering for insurance cost analysis.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Preparing Your Data and Setting a BaseLine

Master data preprocessing fundamentals: load datasets with Pandas, visualize patterns, handle missing values, encode categorical variables, and establish baseline model performance.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Content-Based Recommendation Systems using JS

Master content-based recommendation systems using JavaScript, from basic item profiling to advanced factorization machines for personalized recommendations.

• CodeSignal
• 4 hours
• Self-Paced
• Free Certificate

Corporate Finance Institute

Data Prep for Machine Learning in Python

Master data preparation techniques for machine learning in Python, from importing and cleaning to feature engineering and selection. Learn imputation, EDA, and transformations for optimal ML model performance.

• Coursera
• 5 hours 47 minutes
• Self-Paced
• Paid Course

StatQuest with Josh Starmer

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.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Keith Galli

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.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video

Valerio Velardo - The Sound of AI

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.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

LiquidBrain Bioinformatics

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.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

PyCon US

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.

• YouTube
• 2 hours 17 minutes
• Self-Paced
• Conference Talk

Donato Capitella

Loss Function and Gradient Descent in Neural Network Training - Part 3.1

Dive into neural network training fundamentals, covering dataset preparation, loss functions, and gradient descent optimization techniques with practical examples and mindmap resources.

• YouTube
• 10 minutes
• Self-Paced
• Free Video

Samuel Chan

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.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Donato Capitella

Training MNIST Perceptron with PyTorch - Lab 3.4

Master PyTorch implementation of MNIST digit recognition through hands-on training, including data preparation, model development, and performance optimization techniques.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

5 Minutes Engineering

Label Encoding and OneHot Encoding - Scikit-learn Preprocessing

Master label encoding and one-hot encoding techniques using scikit-learn for effective categorical data preprocessing in machine learning projects.

• YouTube
• 13 minutes
• Self-Paced
• Free Video