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

## 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

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

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

3- Gradient Boosting

Introduction to gradient boosting