The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

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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

<h2> Probabilistic approach for clustering </h2>

KMeans does the hard assignment of the data points in the clusters. KMeans also provides spherical shape clusters. That is not always better with the given data. The cluster can be formulated of any shape. 

Probabilistic models do the soft assignment. For each data point, they tell the probability of assigning in each cluster. They also account for cluster shape and learn weights in dimensions. 


Learn about the probabilistic technique to perform clustering. This lesson introduces the Gaussian distribution and expectation-maximization algorithms to perform clustering.

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

Probabilistic Clustering

Probabilistic approach for clustering