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Course Curriculum
| Chapter 1 | |||
| Overview of Predictive Analytics | 00:00:00 | ||
| Tools in Predictive Analytics | 00:00:00 | ||
| Analytics Life Cycle | 00:00:00 | ||
| Matrix Notation | 00:00:00 | ||
| Chapter 2 | |||
| Basic Foundation | 00:00:00 | ||
| Model, Method and Feature Selection | 00:00:00 | ||
| Chapter 3-4 | |||
| Overview of R for Predictive Modeling | 00:00:00 | ||
| Chapter 5 | |||
| Covariance, Correlation and ANOVA Review | 00:00:00 | ||
| Simple Linear Regression | 00:00:00 | ||
| Regression Assumptions | 00:00:00 | ||
| Chapter 7 | |||
| Multiple Regression | 00:00:00 | ||
| Coefficient of Determination (R Squared) | 00:00:00 | ||
| Dummy Variables in Regression | 00:00:00 | ||
| Data Analysis with Excel | 00:00:00 | ||
| Chapter 8 | |||
| Categorical to Dummy Variables | 00:00:00 | ||
| Polynomials | 00:00:00 | ||
| Box-Cox Transformations | 00:00:00 | ||
| Count Data Models | 00:00:00 | ||
| Centering | 00:00:00 | ||
| Standardization | 00:00:00 | ||
| Rank Transformations | 00:00:00 | ||
| Lagging Data (Causal Models) | 00:00:00 | ||
| Data Reduction | 00:00:00 | ||
| Chapter 10 | |||
| Machine Learning Overview | 00:00:00 | ||
| Overview of Bias and Variance | 00:00:00 | ||
| Cross-Validation in Machine Learning | 00:00:00 | ||
| Chapter 12 | |||
| Dimensionality Reduction | 00:00:00 | ||
| Chapter 13 | |||
| Multicollinearity | 00:00:00 | ||
| Chapter 14 | |||
| Logistic Regression for Machine Learning | 00:00:00 | ||
| Multinomial Regression | 00:00:00 | ||
| Chapter 15 | |||
| Regularization in Machine Learning | 00:00:00 | ||
| Ridge Regression | 00:00:00 | ||
| Lasso | 00:00:00 | ||
| Chapter 17 | |||
| Machine Learning Classifiers | 00:00:00 | ||
| Decision Trees | 00:00:00 | ||
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