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LinkedIn Learning

Supervised Learning Essential Training

via LinkedIn Learning

Overview

This mid-level course takes you through how to create one of the most common types of machine learning: supervised learning models.

Syllabus

Introduction
  • Supervised machine learning and the technology boom
  • Using the exercise files
  • What you should know
1. Supervised Learning with Python
  • What is supervised learning?
  • Python supervised learning packages
  • Predicting with supervised learning
2. Regression Modeling
  • Defining logistic and linear regression
  • Steps to prepare data for modeling
  • Checking your dataset for assumptions
  • Creating a linear regression model
  • Creating a logistic regression model
  • Evaluating regression model predictions
3. Decision Trees
  • Identify common decision trees
  • Splitting data and limiting decision tree depth
  • How to build a decision tree
  • Creating your first decision trees
  • Analyzing decision tree performance
  • Exploring how ensemble methods create strong learners
4. K-Nearest Neighbors
  • Discovering your k-nearest neighbors
  • What's the big deal about k
  • How to assemble a KNN model
  • Building your own KNN
  • Deciphering KNN model metrics
  • Searching for the best model
5. Neural Networks
  • Biological vs. artificial neural networks
  • Preprocessing data for modeling
  • How neural networks find patterns in data
  • Assembling your neural networks
  • Comparing networks and selecting final models
Conclusion
  • Ethical overview
  • How can I keep developing my skills in supervised learning?

Taught by

Ayodele Odubela

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