Professional Certificate in Data-driven Predictive Modeling Interpretation
-- ViewingNowThe Professional Certificate in Data-driven Predictive Modeling Interpretation is a vital course designed to equip learners with the skills to create and interpret predictive models. In today's data-driven world, the ability to leverage predictive modeling for decision-making is increasingly important.
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⢠Introduction to Data-driven Predictive Modeling: Overview of predictive modeling, its applications, and benefits. Understanding the data mining process and the role of predictive modeling in business decision making.
⢠Data Preparation for Predictive Modeling: Data preprocessing techniques, data cleaning, and data transformation. Feature selection and feature engineering.
⢠Regression Analysis: Simple and multiple linear regression. Regression diagnostics, model validation, and assessment of model performance.
⢠Logistic Regression: Binary and multinomial logistic regression. Odds ratios and logit functions. Model validation and assessment of model performance.
⢠Decision Trees and Random Forests: Decision tree construction, overfitting, and pruning. Ensemble methods and random forests.
⢠Support Vector Machines (SVM) and Kernel Methods: SVM formulation and optimization. Kernel functions and kernel methods.
⢠Unsupervised Learning and Dimensionality Reduction: Clustering algorithms, hierarchical clustering, k-means clustering. Principal component analysis (PCA) and singular value decomposition (SVD).
⢠Model Evaluation and Selection: Model validation techniques, cross-validation, and bootstrapping. Model selection criteria and model comparison.
⢠Interpretation of Predictive Models: Understanding model coefficients, feature importance, and marginal effects. Model transparency and explainability.
⢠Ethics and Bias in Predictive Modeling: Ethical considerations in predictive modeling, understanding and mitigating biases in models.
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