Artificial neural networks are a crude abstraction of real neurons. There are various reasons that make AI researchers look at the human brain as an inspiration to develop such networks.
The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
I would highly recommend you to read The Data Science Handbook. The data scientists in the book have helped create the very industry that is now having such a tremendous impact on the world. They discuss the mindset that allowed them to create this industry, address misconceptions about the field, share stories of specific challenges and victories, and talk about what they look for when building their teams.
At the moment, data scientists are getting a lot of attention, and as a result, books about data science are proliferating. While searching for good books about the space, it seems to me that the majority of them focus more on the tools and techniques rather than the nuanced problem-solving nature of the data science process. That is until I encountered Brian Godsey’s “Think Like a Data Scientist.”
From machine translation that connects humans across cultures, to conversational chatbots that help with customer service; from sentiment analysis that deeply understands a human’s mood, to attention mechanisms that can mimic our visual attention, the field of NLP is too expansive to cover completely, so I’d encourage you to explore it further, whether through online courses, blog tutorials, or research papers.
NLP is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Fully understanding and representing the meaning of language is an extremely difficult goal. Why? Because the human language is quite special.
Data mining is the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. In this article, I want to share the 10 mining techniques that I believe any data scientists should learn to be more effective while handling big datasets.