# Tag: Naive Bayes (18)

**Machine Learning – it’s all about assumptions**- Feb 11, 2021.

Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.**All Machine Learning Algorithms You Should Know in 2021**- Jan 4, 2021.

Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.**How to Explain Key Machine Learning Algorithms at an Interview**- Oct 19, 2020.

While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.**Spam Filter in Python: Naive Bayes from Scratch**- Jul 8, 2020.

In this blog post, learn how to build a spam filter using Python and the multinomial Naive Bayes algorithm, with a goal of classifying messages with a greater than 80% accuracy.**KDnuggets™ News 20:n23, Jun 10: Largest Dataset you analyzed? If you start statistics all over again, where would you start? GPT-3**- Jun 10, 2020.

#BlackLivesMatter. In this issue: If you had to start statistics all over again, where would you start? New Poll: What was the largest dataset you analyzed? Another Great NLP Course from Stanford; Naive Bayes: Everything you need to know; GPT-3 - a giant leap for Deep Learning and NLP?**Naïve Bayes Algorithm: Everything you need to know**- Jun 8, 2020.

Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.**Probability Learning: Naive Bayes**- Nov 26, 2019.

This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.**How Bayes’ Theorem is Applied in Machine Learning**- Oct 28, 2019.

Learn how Bayes Theorem is in Machine Learning for classification and regression!**Probability Learning: Bayes’ Theorem**- Oct 16, 2019.

Learn about one of the fundamental theorems of probability with an easy everyday example.**3 Main Approaches to Machine Learning Models**- Jun 11, 2019.

Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.**7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition**- Jun 3, 2019.

This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!**Naive Bayes: A Baseline Model for Machine Learning Classification Performance**- May 7, 2019.

We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.**Naive Bayes from Scratch using Python only – No Fancy Frameworks**- Oct 25, 2018.

We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.**Unfolding Naive Bayes From Scratch**- Sep 25, 2018.

Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.

**Machine Learning Finds “Fake News” with 88% Accuracy**- Apr 12, 2017.

In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.**Top 10 Data Mining Algorithms, Explained**- May 21, 2015.

Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.**Top KDnuggets tweets, Oct 14-15: Tutorial: The Naive Bayes Text Classifier; Quantum Computers and Machine Learning**- Nov 28, 2013.

Tutorial: The Naive Bayes Text Classifier; How Quantum Computers and Machine Learning Will Revolutionize #BigData; See how easy it is to find patterns in random data; Applied Data Science - free, self-guided online course