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
Tree-based learning algorithms, also known as #key# CART: Classification and Regression Trees #key#, are popular for predicting numeric and categorical outputs.

Tree-based methods (decision trees, bagging, random forests, boosting, etc.) are highly effective for supervised learning. This is partly due to their high accuracy and versatility, as they can predict discrete and continuous outcomes.

We will go over three tree-based algorithms:
- Decision Trees
- Random Forests
- Gradient Boosting

# Decision Trees
[**Decision Trees (DT)**](https://www.educative.io/courses/guide-to-machine-learning-python/1-decision-trees) create decision structures to interpret patterns by splitting data into groups. They use variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different classes). The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they are easy to explain to non-experts.




This lesson will provide an overview of tree-based methods like decision trees, random forest, and gradient boosting.

Introduction to tree-based methods

Introduction to Tree-Based Methods

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

Introduction to Tree-Based Methods

Quick Overview