DSTar.tar.gz

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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

Decision trees are commonly used in financial domains due to their ability to solve and explain prediction problems. They are also used as a module in other algorithms to solve more complex algorithms like bagging and boosting.


To understand the decision trees, let's look at the problem of a car loan defaulter prediction. When we want to get a  loan from the bank, they ask some questions and derive answers from our past credit history. 
Questions may relate to: 
- Monthly income
- Personal information
- Previous loans
- Current properties


Learn how to use the decision tree as a classification algorithm.

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

Decision Trees