Supervised Learning Techniques
Supervised Learning Techniques (1 day)
In this course you will learn the most important methodologies, algorithms and ideas of supervised learning techniques. You will learn the essentials of feature and target engineering, and the power of supervised learning techniques to uncover underlying patterns or concepts contained in large datasets, classify objects into predefined categories, and refine your features to enrich predictive modeling endeavors. This course covers the most important algorithms of supervised learning an introduction into modern deep learning modeling approaches will be covered as well. The course will cover modern thinking on model evaluation, model selection, and novel ideas of model deployment. Exercises are offered for KNIME, R, Python, SAS and Spark.
Course Outline
- Introduction to supervised learning and challenges
- Introduction to feature engineering
- Target variable engineering
- Data cleansing (missing value treatment, outlier treatment)
- Sampling Methodology
- Decision Trees
- Regression Analysis
- Neural Networks & Deep Learners
- Naïve Bayes
- Support Vector Machines
- Ensemble Modeling
- Model Assessment
- Model Deployment
Who should attend
Data Scientists, statisticians, business analysts, market researchers, and information technology professionals who need to get started with supervised learning and want to make better use of their data.
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