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

Weka is an open-source machine learning software that provides a graphical workbench to create machine learning models. It is commonly used for industrial applications. It was originally developed in Java, and provides a package that can be directly accessed using the Java program. This means that Java developers can use Weka to perform machine learning in code.

Weka was created by the machine learning group of the University of Waikato.

Weka can be downloaded and installed from https://www.cs.waikato.ac.nz/ml/weka/

Here is what the initial screen of Weka looks like:


Learn about the Java-based machine learning library Weka. Weka  also supports numerous plugins, which can be used to enhance its functionality. 

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

Weka