Robust Regression for Machine Learning in Python. In this tutorial, you will discover robust regression algorithms for machine learning.

Regression is a modeling task that involves predicting a numerical value given an input.

Algorithms used for regression tasks are also referred to as “_regression_” algorithms, with the most widely known and perhaps most successful being linear regression.

Linear regression fits a line or hyperplane that best describes the linear relationship between inputs and the target numeric value. If the data contains outlier values, the line can become biased, resulting in worse predictive performance. **Robust regression** refers to a suite of algorithms that are robust in the presence of outliers in training data.

In this tutorial, you will discover robust regression algorithms for machine learning.

After completing this tutorial, you will know:

- Robust regression algorithms can be used for data with outliers in the input or target values.
- How to evaluate robust regression algorithms for a regression predictive modeling task.
- How to compare robust regression algorithms using their line of best fit on the dataset.

Let’s get started.

How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

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Python For Machine Learning | Machine Learning With Python, you will be working on an end-to-end case study to understand different stages in the Machine Learning (ML) life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this an ML model will be built on the dataset to get predictions. You will learn about the basics of scikit-learn library to implement the machine learning algorithm.

Python for Machine Learning | Machine Learning with Python, you'll be working on an end-to-end case study to understand different stages in the ML life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this, an ML model will be built on the dataset to get predictions. You will learn about the basics of the sci-kit-learn library to implement the machine learning algorithm.