Results for "cross validation"
Skills you'll gain: Decision Tree Learning, Data Preprocessing, Data Transformation, Supervised Learning, Feature Engineering, Scikit Learn (Machine Learning Library), Classification Algorithms, Model Evaluation, Pandas (Python Package)
Coursera
Skills you'll gain: Data Pipelines, MLOps (Machine Learning Operations), Systems Design, Transfer Learning, Data Preprocessing, Data Quality, Feature Engineering, Deep Learning, Model Evaluation, Python Programming, Data Validation, Scikit Learn (Machine Learning Library), Predictive Modeling, Software Engineering, Applied Machine Learning, Debugging, Supervised Learning, Git (Version Control System), Performance Metric, Statistical Analysis
Skills you'll gain: Model Deployment, Model Evaluation, Unsupervised Learning, Transfer Learning, Tensorflow, Keras (Neural Network Library), Dimensionality Reduction, Deep Learning, Recurrent Neural Networks (RNNs), Applied Machine Learning, BeeAI, Data Preprocessing, Convolutional Neural Networks, PyTorch (Machine Learning Library), Responsible AI, Python Programming, Agentic systems, Artificial Intelligence, Artificial Neural Networks, Feature Engineering
Skills you'll gain: MLOps (Machine Learning Operations), Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Containerization, AI Workflows, Model Evaluation, Model Deployment, Generative AI Agents, LangGraph, Keras (Neural Network Library), Transfer Learning, Artificial Intelligence and Machine Learning (AI/ML), CrewAI, BeeAI, Image Analysis, Deep Learning, Large Language Modeling, Natural Language Processing, Vision Transformer (ViT), Python Programming
What brings you to Coursera today?
Skills you'll gain: MLOps (Machine Learning Operations), Model Evaluation, Business Metrics, Feature Engineering, Predictive Modeling, Random Forest Algorithm, Supervised Learning, Scikit Learn (Machine Learning Library), Data Preprocessing, Applied Machine Learning, Performance Metric, Regression Analysis, Continuous Monitoring, Statistical Methods, Algorithms
Skills you'll gain: Model Evaluation, Decision Tree Learning, Data Preprocessing, Classification Algorithms, Applied Machine Learning, Java, Predictive Modeling, Machine Learning Software, Supervised Learning, Technical Communication, Feature Engineering, Tree Maps, MLOps (Machine Learning Operations), Machine Learning Algorithms, Machine Learning, Algorithms
Skills you'll gain: Model Evaluation, Supervised Learning, Feature Engineering, Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Statistical Machine Learning, Predictive Modeling, Artificial Intelligence and Machine Learning (AI/ML), Data Preprocessing, Scikit Learn (Machine Learning Library), Random Forest Algorithm, Regression Analysis, Classification Algorithms, Logistic Regression, Performance Tuning
Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering
Whizlabs
Skills you'll gain: Supervised Learning, Unsupervised Learning, MLOps (Machine Learning Operations), Applied Machine Learning, Model Evaluation, AWS SageMaker, Data Preprocessing, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Cloud Computing, Azure DevOps, Model Deployment, Microsoft Azure, Amazon Web Services, Deep Learning, Data Processing
In summary, here are 10 of our most popular cross validation courses
- Classification Trees in Python, From Start To Finish: Coursera
- ML Model Training & Validation: Coursera
- Validate, Analyze, and Monitor ML Models: Coursera
- AI Engineer Associate: Packt
- AI Engineer Professional: Packt
- Validate and Explain Your ML Models: Coursera
- Build Predictive & Supervised Models: Coursera
- Predict and Validate Regression Models in R: Coursera
- Build & Evaluate Decision Trees for ML: Coursera
- Core Machine Learning & Evaluation: Packt
