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 random forests
While decision trees are useful for explaining a model’s decision structure, they are also prone to overfitting.

In general, decision trees are accurate at decoding patterns using the training data, but since there is a fixed sequence of decision paths, any variance in the test data or any new data can result in poor predictions. The fact that there is only one tree design also limits the flexibility of this method to manage variance and future outliers.

A solution to mitigate overfitting is to grow multiple trees using a different technique called #key# RF: Random Forests #key#. This method involves growing multiple decision trees using a randomized selection of input data for each tree and combining the results by averaging the output for regression or class voting for classification.

The variables selected to divide the data are also randomized and capped. If the entire forest inspected a full set of variables, each tree would look similar. The trees would attempt to maximize information gain at the subsequent layer and select the optimal variable at each split.

Unlike a standard decision tree with a full set of variables to draw from, though, the random forests algorithm has an artificially limited set of variables available to build decisions. Due to fewer variables shown and the randomized data provided, random forests are less likely to generate a collection of similar trees. Embracing randomness and volume, random forests are subsequently capable of providing a reliable result with potentially less variance and overfitting than a single decision tree.

The following steps are involved in the implementation of RF:

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

## Exercise 
This exercise is a repeat attempt of the previous one, using the Advertising dataset and the same dependent and independent variables but built using `RandomForestClassifier` from `Scikit-learn.`

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# Introduction to random forests
While decision trees are useful for explaining a model’s decision structure, they are also prone to overfitting.

In general, decision trees are accurate at decoding patterns using the training data, but since there is a fixed sequence of decision paths, any variance in the test data or any new data can result in poor predictions. The fact that there is only one tree design also limits the flexibility of this method to manage variance and future outliers.

A solution to mitigate overfitting is to grow multiple trees using a different technique called #key# RF: Random Forests #key#. This method involves growing multiple decision trees using a randomized selection of input data for each tree and combining the results by averaging the output for regression or class voting for classification.

The variables selected to divide the data are also randomized and capped. If the entire forest inspected a full set of variables, each tree would look similar. The trees would attempt to maximize information gain at the subsequent layer and select the optimal variable at each split.

Unlike a standard decision tree with a full set of variables to draw from, though, the random forests algorithm has an artificially limited set of variables available to build decisions. Due to fewer variables shown and the randomized data provided, random forests are less likely to generate a collection of similar trees. Embracing randomness and volume, random forests are subsequently capable of providing a reliable result with potentially less variance and overfitting than a single decision tree.

The following steps are involved in the implementation of RF:

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

# Exercise 
This exercise is a repeat attempt of the previous one, using the Advertising dataset and the same dependent and independent variables but built using `RandomForestClassifier` from `Scikit-learn.`

---


This lesson will provide an overview of random forests and go over the steps involved in their implementation.

Random Forests

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

2- Random Forests

Introduction to random forests