Professional Certificate in Predictive Model Building Expertise
-- ViewingNowThe Professional Certificate in Predictive Model Building Expertise is a comprehensive course that equips learners with essential skills in predictive modeling. This certificate program emphasizes the importance of data-driven decision making, which is crucial in today's data-centric world.
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⢠Introduction to Predictive Modeling: Overview of predictive model building, its applications, and benefits. Understanding the differences between regression, classification, and time series analysis.
⢠Data Preparation: Data cleaning, preprocessing, and exploratory data analysis. Handling missing data, outliers, and categorical variables. Feature scaling, transformation, and engineering.
⢠Statistical Foundations: Probability distributions, statistical inference, hypothesis testing, and confidence intervals. Understanding the assumptions of predictive models and their implications.
⢠Model Evaluation Metrics: Evaluating the performance of predictive models using accuracy, precision, recall, F1-score, R-squared, mean absolute error, mean squared error, and other metrics.
⢠Regression Analysis: Simple and multiple linear regression, polynomial regression, and logistic regression. Understanding the assumptions, advantages, and limitations of these models.
⢠Classification Techniques: Decision trees, random forests, support vector machines, and k-nearest neighbors. Ensemble methods, such as bagging, boosting, and stacking.
⢠Time Series Analysis: Autoregressive (AR), moving average (MA), and autoregressive moving average (ARIMA) models. Seasonality, trends, and stationarity. Forecasting techniques and performance evaluation.
⢠Model Selection and Tuning: Model validation techniques, such as k-fold cross-validation and bootstrapping. Grid search, random search, and Bayesian optimization for hyperparameter tuning. Overfitting, underfitting, and model complexity.
⢠Deploying Predictive Models: Integrating predictive models into production environments. Containerization, version control, and monitoring performance. Ethical considerations and model transparency.
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