10 reasons why data scientists love Jupyter notebooks

1. All in one place
2. Easy to share
3. Easy to convert
4. Language independent
5. Easy to create kernel wrappers
6. Easy to customize
7. Extensions with custom magic commands
8. Stress-free Reproducible experiments
9. Effective teaching-cum-learning tool
10. Interactive code and data exploration

Monte Carlo method in Python

In this post, we will explore our first reinforcement learning methods for estimating value. It’s the first taste of real RL in this series. I bet you’ve heard the term Monte Carlo method before.

Featuretools: An open source framework for automated feature engineering

Featuretools is a framework to perform automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning.

It’s About Augmented Intelligence, not Artificial Intelligence

The adoption of AI applications isn’t about replacing workers but helping workers do their jobs better.

Brexit Tweets Sentiment Analysis in Python

Sentiment analysis is a method of analyzing a piece of text and deciding whether the writing is positive, negative or neutral. It is commonly used to understand how people feel about a topic. E.g – What people think about Trump winning the next election or Ussain Bolt fining in 7 seconds. These days the information is hugely recorded on social media platforms and a lot of predictions like who will win the election or a match is being done through that recorded information.

Adding macOS Touch Bar Support to RStudio

Modern MacBook Pros have a fairly useless (c’mon, admit it!) “Touch Bar” that did little more than cause severe ire in the developer community after turning a full-fledged, tactile Escape key into a hollow version if its former self. Having said, that, some apps do make OK use of it, with Fantastical and Omnigraffle being two of the better implementations.