DSTar.tar.gz

code-job

r-job

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


## Supervised learning

Supervised learning refers to inferring a function from a labeled data set. We have been given data as features, and we have to predict the output. This output can be from the predefined set of values (like predicting the animal or type of vehicle) or from a continuous range (like predicting stock price or the temperature during the day). These types of problems utilize the approach of supervised learning, which uses the concepts of regression. 



# Supervised learning

Supervised learning refers to inferring a function from a labeled data set. We have been given data as features, and we have to predict the output. This output can be from the predefined set of values (like predicting the animal or type of vehicle) or from a continuous range (like predicting stock price or the temperature during the day). These types of problems utilize the approach of supervised learning, which uses the concepts of regression. 


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

Simple Regression

Supervised learning