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

## Introduction to gradient boosting
Like random forests, boosting provides yet another regression/classification technique for aggregating the outcome of multiple decision trees.

Rather than building random independent variants of a decision tree in parallel, #key# GB: Gradient Boosting #key# is a sequential method that aims to improve the performance of each subsequent tree. This works by evaluating the performance of weak models and then overweighting subsequent models to mitigate the outcome of instances misclassified in earlier rounds. Instances classified correctly at the previous round are replaced with a higher proportion of instances that weren’t accurately classified. 

While this creates another weak model, the modifications derived from the previous model help the new model to key in on the previous tree’s mistakes. The adaptability of the algorithm to learn from its mistakes makes gradient boosting one of the most popular algorithms in machine learning today.

The following steps are involved in the implementation of GB:

- 1 - Import libraries
- 2 - Import dataset
- 3 - Convert non-numeric variables
- 4 - Remove variables
- 5 - Set X and y variables
- 6 - Set algorithm
- 7 - Evaluate

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# Introduction to gradient boosting
Like random forests, boosting provides yet another regression/classification technique for aggregating the outcome of multiple decision trees.

Rather than building random independent variants of a decision tree in parallel, #key# GB: Gradient Boosting #key# is a sequential method that aims to improve the performance of each subsequent tree. This works by evaluating the performance of weak models and then overweighting subsequent models to mitigate the outcome of instances misclassified in earlier rounds. Instances classified correctly at the previous round are replaced with a higher proportion of instances that weren’t accurately classified. 

While this creates another weak model, the modifications derived from the previous model help the new model to key in on the previous tree’s mistakes. The adaptability of the algorithm to learn from its mistakes makes gradient boosting one of the most popular algorithms in machine learning today.

The following steps are involved in the implementation of GB:

- 1 - Import libraries
- 2 - Import dataset
- 3 - Convert non-numeric variables
- 4 - Remove variables
- 5 - Set X and y variables
- 6 - Set algorithm
- 7 - Evaluate

---

This lesson will provide an overview of gradient boosting and the steps involved in its implementation.

Gradient Boosting

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

3- Gradient Boosting

Introduction to gradient boosting