The number of Machine Learning algorithms keeps increasing with time. If you want to be a successful Data Scientist, you must understand how they differ from one another.

This story is part of a series where I provide an in-depth look at different algorithms, how they work, and how to build them in Python.

- Visual comparison of model predictions between a Single Decision Tree, Random Forest, and AdaBoost.
- Explanation of how AdaBoost differs from other algorithms
- Python code examples

Let us start with a comparison of the prediction probability surface made by each of the three models. …

If you want to be a successful Data Scientist, you need to understand the nuances of different Machine Learning algorithms.

This story is part of a series where I provide an in-depth look at how such algorithms work. This includes simple examples, 3D visualizations, and complete Python code for you to use in your Data Science projects.

- The category of algorithms Random Forest classification belongs to
- An explanation of how Random Forest classification works and why it is better than a single decision tree
- Python code examples and visualizations

Similar to some other algorithms, Random Forest can handle both classification…

If you want to be a successful Data Scientist, it is essential to understand how different Machine Learning algorithms work.

This story is part of the series that explains the nuances of each algorithm and provides a range of Python examples to help you build your own ML models. Not to mention some cool 3D visualizations!

- The category of algorithms that CART belongs to
- An explanation of how the CART algorithm works
- Python examples on how to build a CART Decision Tree model

As the name suggests, CART (Classification and Regression Trees) can be used for both classification and regression…

It is essential to understand how different Machine Learning algorithms work to succeed in your Data Science projects.

I have written this story as part of the series that dives into each ML algorithm explaining its mechanics, supplemented by Python code examples and intuitive visualizations.

- The category of algorithms that SVM classification belongs to
- An explanation of how the algorithm works
- What are kernels, and how are they used in SVM?
- A closer look into RBF kernel with Python examples and graphs

Support Vector Machines (SVMs) are most frequently used for solving **classification** problems, which fall under the supervised machine…

*Just so you know what you are getting into, this is a **long story** that contains a mathematical explanation of the Naive Bayes classifier with 6 different Python examples. Please take a look at the **list of topics below** and feel free to jump to the most interesting sections for you.*

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. …

*Just so you know what you are getting into, this is a **long story** that contains a visual and a mathematical explanation of logistic regression with 4 different Python examples. Please take a look at the **list of topics below** and feel free to jump to the sections that you are most interested in.*

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. …

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

- The category of algorithms that SVR belongs to
- An intuitive explanation of how SVR works
- A few examples of how to build SVR models in Python

While you may not be familiar with SVR, chances are you have previously…

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

- What category of algorithms does LOWESS belong to?
- How does Locally Weighted Scatterplot Smoothing work?
- How can I use LOWESS to identify patterns and predict new data in Python?

Locally Weighted Scatterplot Smoothing sits within the family of regression…

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

Before we dive into the specifics of MARS, I assume that you are already familiar with Linear Regression. If you would like a refresher on the topic, feel free to explore my linear regression story:

- What category of algorithms…

Machine Learning is making huge leaps forward, with an increasing number of algorithms available so we can solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it will also provide you Python examples so you can use them to build your own ML models.

- What category of algorithms does linear regression belong to?
- What problems can be solved using linear regression?
- How does the linear regression algorithm work?
- How can I use linear regression to build a…

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