Discovering your k-nearest neighbors - Python Tutorial

Contents

• Introduction Introduction

  • Supervised machine learning and the technology boom

    59s
  • Using the exercise files

    58s
  • What you should know

    34s
• 1. Supervised Learning with Python 1. Supervised Learning with Python

  • What is supervised learning?

    3m 9s
  • Python supervised learning packages

    56s
  • Predicting with supervised learning

    2m 5s
• 2. Regression Modeling 2. Regression Modeling

  • Defining logistic and linear regression

    2m 51s
  • Steps to prepare data for modeling

    4m 31s
  • Checking your dataset for assumptions

    7m 18s
  • Creating a linear regression model

    2m 51s
  • Creating a logistic regression model

    4m 28s
  • Evaluating regression model predictions

    2m 40s
• 3. Decision Trees 3. Decision Trees

  • Identify common decision trees

    1m 55s
  • Splitting data and limiting decision tree depth

    3m 41s
  • How to build a decision tree

    2m 3s
  • Creating your first decision trees

    2m 49s
  • Analyzing decision tree performance

    5m 1s
  • Exploring how ensemble methods create strong learners

    1m 55s
• 4. K-Nearest Neighbors 4. K-Nearest Neighbors

  • Discovering your k-nearest neighbors

    3m 7s
  • What's the big deal about k

    2m 7s
  • How to assemble a KNN model

    1m 53s
  • Building your own KNN

    7m 13s
  • Deciphering KNN model metrics

    4m 12s
  • Searching for the best model

    3m 13s
• 5. Neural Networks 5. Neural Networks

  • Biological vs. artificial neural networks

    3m 5s
  • Preprocessing data for modeling

    2m 25s
  • How neural networks find patterns in data

    2m 8s
  • Assembling your neural networks

    2m 55s
  • Comparing networks and selecting final models

    1m 56s
• Conclusion Conclusion

  • Ethical overview

    2m 57s
  • How can I keep developing my skills in supervised learning?

    33s