Advanced Scikit Learn Training

Scikit-Learn is a robust and widely used Python Machine Learning library. It provides a range of supervised and unsupervised learning algorithms. It also provides many useful utility functions such as model selection, hyper parameter tuning, feature extraction, data preprocessing. This course is a follow up from our basic Scikit Learn course. The topics include

• Feature Extraction
• Pipelines
• Discussing Ensemble Methods in Detail
• Hyper Parameter Tunning
• Model Metrics

Module 1 Feature Extraction

• Feature Hasing
• Text Feature Extraction
• TF-IDF

Module 2 Data Preprocessing

• - Scaling the Data
• - Normalizing the Data
• - Label Encoding

Module 3 Pipelines and Composite Estimators

• Pipelining Estimators
• Composite Estimator

Module 4 Module Hyper Parameter Tuning

• Exhaustive Grid Search
• Randomized Parameter Optimizatin
• Cross Validation
• Out of Bags Estimates
• Valdidation Curves
• Learning Curves

Module 4 Ensemble Estimators

• Bagging Estimators
• Boosting Estimators 
• Stacking Estimators 

Module 6 Model Evaluation

• Classification Metrics
• Regression Metrics
• Clustering Metrics