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Data Science: Supervised Machine Learning in Python

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Overview

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn What you'll learn: Understand and implement K-Nearest Neighbors in PythonUnderstand the limitations of KNNUser KNN to solve several binary and multiclass classification problemsUnderstand and implement Naive Bayes and General Bayes Classifiers in PythonUnderstand the limitations of Bayes ClassifiersUnderstand and implement a Decision Tree in PythonUnderstand and implement the Perceptron in PythonUnderstand the limitations of the PerceptronUnderstand hyperparameters and how to apply cross-validationUnderstand the concepts of feature...

Syllabus

  • Introduction and Review
  • K-Nearest Neighbor
  • Naive Bayes and Bayes Classifiers
  • Decision Trees
  • Perceptrons
  • Practical Machine Learning
  • Building a Machine Learning Web Service
  • Conclusion
  • Appendix / FAQ Finale
  • Setting Up Your Environment (FAQ by Student Request)


Data Science: Supervised Machine Learning in Python
Go to Class

via Udemy

6 hours 24 minutes

Certificate Available

English

On-Demand

Beginner

Instructor

Lazy Programmer Team

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