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

NLP is the technique used to make the computer understand human language and react accordingly. The purpose of NLP is to fill the gap between human communication and computer understanding. Today we can see a good number of NLP powered systems around us, like:
- Alexa, Ok Google, Siri: Voice assistants that respond to human commands  and perform the tasks accordingly
- Search engine recommendations: We type on the search bar and get the relevant results 
- Audio to text conversions: We type as we speak 
- Language translation: Conversion from one language to another 
- Chatbots: Talking and discussing things with a human via online chatrooms


Introduction to Natural Language Processing (NLP) and its capabilities.

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

Natural Language Processing