Machine Learning for Application Developers Training

• Learn to translate business problems into machine learning algorithms.
• Know how to treat unstructured and semi structured data, such as text, time series, spatial, graph data, and images.
• Understand the generalization error of the model before deployment
• Ensure proper handling of training and testing data so the testing data mimics incoming data when the model is deployed in production
• Selecting the appropriate objective/loss function inspired by the business value is important for ultimate success in the application
• Understanding features in the data and improving upon them (by creating new features and eliminating existing ones) has a high impact in terms of predictability
• Selecting the machine learning method that works best for the given problem is key and often determines success or failure

Lesson 1: Python Ecosystem for Machine Learning. 

Lesson 2: Python and SciPy Crash Course. 

Lesson 3: Load Datasets from CSV. 

Lesson 4: Understand Data With Descriptive Statistics. 

Lesson 5: Understand Data With Visualization. 

Lesson 6: Pre-Process Data. 

Lesson 7: Feature Selection. 

Lesson 8: Resampling Methods. 

Lesson 9: Algorithm Evaluation Metrics. 

Lesson 10: Spot-Check Classification Algorithms. 

Lesson 11: Spot-Check Regression Algorithms. 

Lesson 12: Model Selection. 

Lesson 13: Pipelines. 

Lesson 14: Ensemble Methods. 

Lesson 16: Model Finalization.