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>Hierarchical clustering</h2>

Hierarchical clustering does not restrict the user to choose the number of the clusters before running the algorithm. Users can choose different values of K, and different clusters will be fixed. It uses the dendrogram type of formulation. When choosing a different K, the same computation will be used (no need to rerun the algorithm). We can also capture highly complex shapes of clusters.

Hierarchical clustering can be classified into two types:
<li> Divisive: Top to down approach; put all the data in one cluster and keep dividing it.
<li> Agglomerative: Bottom to up; each data point into a separate cluster and combine cluster and keep moving up to form a single cluster of all data points.

Learn about two types of hierarchical clustering - Divisive Clustering and Agglomerative 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

Hierarchical Clustering

Hierarchical clustering