Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# Quick overview of the pre-model algorithm
As an extension of the data scrubbing process, unsupervised learning algorithms are sometimes used to advance a supervised learning algorithm to prepare the data for prediction modeling. In this way, unsupervised algorithms are used to clean or reshape the data rather than derive actionable insight.

Examples of pre-model algorithms include dimension reduction techniques, as introduced in the previous chapter, and k-means clustering. Both of these algorithms are examined in this chapter.

# PCA
One of the most popular dimension reduction techniques is #key# PCA: Principal Component Analysis #key#. Known as general factor analysis, PCA is useful for dramatically reducing data complexity and visualizing data in fewer dimensions. 

The practical goal of PCA is to find a low-dimensional representation of the dataset that preserves as much about the original variables as possible. Rather than removing individual features from the dataset, PCA recreates dimensions as a linear combination of features called components. It then ranks components that contribute most to patterns in the data, allowing you to drop components that have the least impact on data variability.

In practice, the initial aim of PCA is to place the first axis in the direction of the greatest variance of the data points and maximize the variance depicted along that axis. A second axis is then placed perpendicular (on a 90-degree angle) to the first axis to form an orthogonal line, which creates the first two components.  

In a two-dimensional setting, the location of the second axis is fixed according to the position of the first axis. In a three-dimensional space, where there are more options to place the second axis perpendicular to the first axis, the aim is to position it in a way that maximizes the variance on its axis.
An example of PCA in a two-dimensional space is demonstrated in the following figure.

This lesson will introduce you to the pre-model algorithm and familiarize you with the implementation steps (1-3) of the principal component analysis.

PCA Implementation Steps: 1 to 3

Introduction to Course

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

Conclusion

Appendix

PCA Implementation Steps: 1 to 3

Quick overview of the pre-model algorithm

PCA