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

MLtarball.tar.gz

jupyter1

jupyter2

jupyter3

jupyter4

jupyter5

jupyter6

jupyter7

jupyter8

jupyter9

jupyter10

jupyter11

jupyter12

jupyter13

jupyter14

jupyter15

jupyter16

jupyter17

jupyter18dtextra

jupyter19dt

jupyter20rf

jupyter21gbc

jupyter22gbr

jupyter-temp

pca6

split

lor1

lor2

lor3

lor4

lor5

lor6

lor7

lor8

lor9

svm9

knn9

gbc7

gbr1

gbr2

gbr3

gbr4

gbr5

gbr6

gbr7

gbr8

Mlxtend

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
Dimension reduction, also known as descending dimension algorithms, transforms data to a lower dimension. This can help to lessen computational resources and visualize patterns in the data.

Dimensions are the number of variables describing the data, such as the city of residence, country of residence, age, and gender. Four variables can be plotted on a scatterplot, but three-dimensional and two-dimensional plots are easiest for the human eye to interpret.

The goal of a descending dimension algorithm is to arrive at a minimal set of variables that mimic the distribution of the original dataset’s variables. In addition, reducing the number of variables makes it easier to recognize patterns, including natural groupings, outliers, and anomalies.

It’s important to note that dimension reduction isn’t a case of deleting columns., Rather, it mathematically transforms information in those columns to capture the information using fewer variables (columns). If, for example, you look at house prices, you might find multiple correlated variables (such as house area and postcode) that you can merge into a new variable that adequately represents those two variables. Thus, by applying dimension reduction before running the core algorithm, the model will run faster, consume less computational resources, and may actually provide more accurate predictions.

Another side benefit of this technique is the opportunity to visualize multidimensional data. Remember that the maximum number of plottable dimensions for a scatterplot is four. Although two or three dimensions are ideal (the fourth dimension is time), descending dimension algorithms can be used to streamline a dataset with more than four dimensions into four or fewer variables and project the synthetic variables onto the visual workspace of a scatterplot.

This lesson will introduce you to the concept of dimension reduction.

Data Scrubbing Operation: Dimension Reduction

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

Data Scrubbing Operation: Dimension Reduction

Quick overview