Learn at your own pace with over 40 lectures and more than 5.5 hours of content.
At the end of this course you will be able to:
- Evaluate model bias-variance trade-off which can possibly lead to its under-fitting or over-fitting.
- Assess goodness-of-fit and test forecasting accuracy with scale dependent and scale-independent metrics.
- Calculate generalized linear models such as linear and elastic net regressions.
- Analyze similarity methods such as k nearest neighbors’ regression and frequency methods such as decision tree regression.
- Explore ensemble methods such as random forest and gradient boosting machine regression to advance decision tree prediction accuracy.
- Estimate maximum margin methods such as support vector machines with linear and non-linear kernels.
- Analyze multi-layer perceptron methods such as artificial neural networks.
This course is best for you as:
- Student at any knowledge level who wants to learn the subject.
- Academic researcher who wishes to deepen your knowledge in data mining, applied statistical learning or artificial intelligence.
- Business data scientist who desires to apply these concepts in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.
The requirements for this course are:
- R statistical software is required. Downloading instructions included.
- RStudio Integrated Development Environment is recommended. Downloading instructions included.
- R script files provided with course.
- Familiarity with software is recommended.
- Mathematical formulae kept at minimum essential level for main concepts understanding.
Feel free to take a look at course curriculum here!