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How Naive Bayes Algorithm Works-Webinar Recording

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How Naive Bayes Algorithm Works-Webinar Recording

Naive Bayes is a powerful algorithm for predictive modelling & a probabilistic machine learning model. It is used or a variety of classification tasks like filtering spam, sentiment prediction, classifying documents etc.

Naive Bayes algorithm is the most popular topic in the Data Science Community and at the same time, it is a bit complex to understand.

Machine learning at its core has to do with defining a problem statement probabilistically and using optimization methods to train a model. That’s why the most tried and test probabilistic model, Naive Bayes is something that every data scientist must know and understand.

To cater to the need of aspiring Data Scientists and Data Science professionals, we recently conducted a webinar on the working of Naive Bayes Algorithm.

We had with is Akshay Sehgal, General Manager, Data Science, Reliance Industries Limited, who gave valuable insights on How Naive Bayes Algorithm Works.

Akshay is proficient in designing, training and deploying machine learning models. He has expertise in building new age enterprise-scale data science solutions across multiple domains.

Key Takeaways:

1. A different perspective on machine learning
2. Bayes theorem and the ‘naive’ assumption
3. How does the Naive Bayes algorithm work in practice
4. Different classifiers for different types of data
5. Behind the scenes of NB, from the lens of linear algebra
6. Implementing NB classifiers from scratch
These were some of the highlights of the session. To get in-depth details of “How Naive Bayes Algorithm Works”, access the webinar recording.
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