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This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

This Naive Bayes technique is based on probabilities. The probability of an event happening or not happening can be calculated using past data.

Consider the problem of predicting the temperatures for next week using the current temperature. This seems very difficult. However, if you have the last five or ten years of temperature data, you can do this more efficiently and with higher accuracy. The Naïve Bayes algorithm is based on this concept.

Here we will provide some examples for probability and move forward toward building the algorithm. 


Learn about the Bayes' theorem and its application in the Naive Bayes classifier.

Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Naïve Bayes