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- Model Evaluation free courses
Model Evaluation Courses
Model evaluation is crucial in predictive modeling to assess performance and accuracy. It helps determine how well a model generalizes to new data and captures underlying patterns, guiding informed decision-making and iterative improvements.
Free courses on model evaluation teach you to use evaluation metrics, apply cross-validation techniques, and make decisions based on performance measures. These skills are essential for identifying model weaknesses, improving performance, and selecting the best predictive models. Mastering model evaluation ensures reliable, accurate models for data-driven insights and effective decision-making.
Free Model Evaluation Courses
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Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python
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Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms
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Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes
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Skills: Unsupervised Learning,Clustering, k-means Clustering
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Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters
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Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset
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Skills: Feature Engineering, Feature Selection
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Skills: Linear Regression, Logistic Regression, Naïve Bayes
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Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation
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Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?
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Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python
View Course
Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms
View Course
Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes
View Course
Skills: Unsupervised Learning,Clustering, k-means Clustering
View Course
Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters
View Course
Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset
View Course
Skills: Feature Engineering, Feature Selection
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Skills: Linear Regression, Logistic Regression, Naïve Bayes
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Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation
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Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?
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Skills: Feature Engineering, Feature Selection
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Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?
View Course
Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters
View Course
Skills: Linear Regression, Logistic Regression, Naïve Bayes
View Course
Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation
View Course
Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset
View Course
Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python
View Course
Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes
View Course
Skills: Unsupervised Learning,Clustering, k-means Clustering
View Course
Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms
View Course
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Skills: Feature Engineering, Feature Selection
View Course
Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?
View Course
Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters
View Course
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Skills: Linear Regression, Logistic Regression, Naïve Bayes
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Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation
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Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset
View Course
Popular
Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python
View Course
Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes
View Course
Skills: Unsupervised Learning,Clustering, k-means Clustering
View Course
Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms
View Course
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