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Data Science: Supervised Machine Learning in Python

Data Science: Supervised Machine Learning in Python

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Data Science: Supervised Machine Learning in Python Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn 4.6 (767 ratings) 9,024 students enrolled Created by Lazy Programmer Inc.

Data Science: Supervised Machine Learning in Python, Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn
Created by Lazy Programmer Inc.

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What Will I Learn?
  • Understand and implement K-Nearest Neighbors in Python
  • Understand the limitations of KNN
  • User KNN to solve several binary and multiclass classification problems
  • Understand and implement Naive Bayes and General Bayes Classifiers in Python
  • Understand the limitations of Bayes Classifiers
  • Understand and implement a Decision Tree in Python
  • Understand and implement the Perceptron in Python
  • Understand the limitations of the Perceptron
  • Understand hyperparameters and how to apply cross-validation
  • Understand the concepts of feature extraction and feature selection
  • Understand the pros and cons between classic machine learning methods and deep learning
  • Use Sci-Kit Learn
  • Implement a machine learning web service
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