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Machine Learning For Application Developers
Machine learning/AI Training Course duration

5 Days

Machine learning/AI Training Course Objectives
  • 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
How you can use Watson APIs and services
  • Build a cognitive search and content analytics engine
  • Use Watson Discovery to quickly create cognitive applications that extract value from large amounts of structured and unstructured data.
  • Teach your apps to listen and speak
  • Use Speech to Text to embed speech understanding within new or existing applications, in a variety of languages, such as US and UK English, Spanish, Japanese and more.
  • Extract breakthrough insights from texts
  • Use natural language understanding to analyze large volumes of texts in natural language - whether short-form social content or lengthy essays - to understand concepts, entities, keywords, sentiment, and more, quickly and easily.
  • Automate customer interactions
  • Use Watson Conversation to create a bot powered by natural language understanding in minutes, then deploy via multiple channels and devices, such as messaging platforms like Slack, mobile devices/SMS, and even physical robots.
  • Train apps to see like a human expert
  • Use Visual Recognition to answer the question, "What's in these images?" by analyzing and classifying the content of pictures.
Machine learning/AI Training Course outline

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.



 
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