Learn what machine learning is and get a high-level view of it with Python.

Machine Learning with Python Libraries

Machine learning is a type of artificial intelligence used with multiple Python libraries. In this module, we’ll learn to code basic machine learning models, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. This module will teach you how to import datasets into Python using pandas. We’ll also learn about tree-based learning algorithms to predict numeric and categorical outputs. If you need to revise the concepts, we have summarized key concepts of machine learning, and the overviews of specific algorithms are dispersed throughout the module.

A Practical Guide to Machine Learning with Python

Using Scikit-learn, the default number of variables using the KNeighborsClassifier is:

Using Scikit-learn, the default number of variables using the KNeighborsClassifier is:

Which of the following evaluation techniques can we use to evaluate the accuracy of a k-nearest neighbors model?

Which of the following evaluation techniques can we use to evaluate the accuracy of a k-nearest neighbors model?

Which metric can we use to compare the accuracy of different k-NN models?

Which metric can we use to compare the accuracy of different k-NN models?

Which algorithm is sometimes used to simplify and prepare a dataset for k-NN analysis?

Which algorithm is sometimes used to simplify and prepare a dataset for k-NN analysis?

k-nearest neighbors is both a classification and a regression technique. True or False?

k-nearest neighbors is both a classification and a regression technique. True or False?

Before We Begin

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Unsupervised Learning

Deep Learning

Conclusion

Appendix

Quiz