Data Science II - Foundations of Machine Learning
Issued by
Colaberry
The earner understands the basic foundations of Machine Learning, with extended knowledge in Math & Statistics, Regression, and Classification models.
- Type Learning
- Level Advanced
- Time Weeks
- Cost Free
Skills
- ANOVA
- Basic Probability
- Chi Square Testing
- Common Distributions
- Hypothesis Testing
- Introduction To Statistics
- Linear Algebra
- Linear Regression Models
- Logistic Regression
- Model Selection
- Naive Bayes
- Outlier Analysis
- Polynomial Regression
- Regression Metrics
- Set Theory
- Supervised And Unsupervised Learning
Earning Criteria
-
Linear Algebra - Introduction to Statistics and Probability - Distributions and Associated Statistics - Statistics Revisited - Linear Regression Models - Model Selection and Polynomial Regression - Classification and Logistic Regression - Classification - Naive Bayes