Cross-Validation Courses and Certifications

Rob Mulla

Complete Guide to Cross Validation

Comprehensive guide to cross-validation in machine learning, covering setup, datasets, common pitfalls, train-test splits, and practical applications using scikit-learn in Python.

• YouTube
• 30 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Cross Validation in Machine Learning - Bias-Variance Trade-off and Validation Techniques

Learn cross-validation techniques for model evaluation, including bias-variance trade-off, validation set approach, LOOCV, and k-fold CV, to improve predictive performance.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Molecular Imaging & Therapy

A Guide to Cross-Validation for AI - Avoiding Overfitting and Ensuring Generalizability

Explore cross-validation techniques for AI, including K-fold and nested methods. Learn to avoid overfitting, ensure generalizability, and select optimal approaches for model evaluation.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Data Science with Python: Enhancing Model Accuracy and Robustness

Simply creating a machine learning model is not enough to gain the best insights on the data. This course will teach you how to enhance a model through hyper-parameter tuning and other methods.

• Pluralsight
• 23 minutes
• On-Demand
• Free Trial Available

Data Science: Machine Learning

90 ratings at edX

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

• edX
• 8 weeks, 2-4 hours a week
• On-Demand
• Free Online Course (Audit)

Introduction to Machine Learning with caret

Master machine learning fundamentals using R's caret package, covering data preprocessing, model training, evaluation, and cross-validation through hands-on practice with real datasets like iris.

• CodeSignal
• On-Demand
• Free Certificate

Practical Machine Learning with the mtcars Dataset in R

Master practical machine learning in R through hands-on projects with mtcars dataset, covering preprocessing, model building, evaluation, visualization, and cross-validation techniques.

• CodeSignal
• On-Demand
• Free Certificate

Hypertuning and Cross-Validation

Master hyperparameter optimization techniques through grid search, random search, and cross-validation methods to enhance machine learning model performance and achieve optimal results.

• CodeSignal
• On-Demand
• Free Certificate

Model Evaluation and Optimization

Master advanced techniques for evaluating and optimizing regression models, from cross-validation to hyperparameter tuning and regularization methods for enhanced predictive accuracy.

• CodeSignal
• On-Demand
• Free Certificate

TensorFlow Techniques for Model Optimization

Master advanced TensorFlow optimization techniques, from regularization and dropout implementation to callback utilization and Scikit-Learn integration, enhancing model performance and preventing overfitting.

• CodeSignal
• On-Demand
• Free Certificate

Building and Evaluating Text Classifiers in Python

Master text classification techniques using Naive Bayes, logistic regression, and grid search optimization. Learn preprocessing, model training, cross-validation, and performance evaluation using Python and the SMS Spam dataset.

• CodeSignal
• On-Demand
• Free Certificate

Introduction to Machine Learning with Gradient Boosting Models

Master gradient boosting models through hands-on practice with Tesla stock predictions, covering model training, cross-validation, hyperparameter tuning, feature importance analysis, and early stopping techniques.

• CodeSignal
• On-Demand
• Free Certificate

Foundational Machine Learning Models with Sklearn

Master fundamental machine learning algorithms using Sklearn and the Iris dataset, from linear regression to decision trees, with hands-on implementation, evaluation metrics, and model optimization techniques.

• CodeSignal
• On-Demand
• Free Certificate

Introduction to Modeling Techniques for Text Classification

Master essential text classification techniques using Naive Bayes, SVM, Decision Trees, Random Forests, and Logistic Regression. Learn preprocessing, model tuning, and evaluation methods for effective NLP implementations.

• CodeSignal
• On-Demand
• Free Certificate

Microcredential

Introduction to Machine Learning with SciKit Learn

Master machine learning fundamentals using SciKit-learn, from data preprocessing through advanced model evaluation, including regression, classification, ensemble methods and hyperparameter tuning.

• CodeSignal
• On-Demand
• Free Certificate
• 7 courses