This post is going to cover some very basic concepts in machine learning, from linear algebra to evaluation metrics. It serves as a nice guide to newbies looking to enter the field.
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.
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.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).
In those moments of boredom when you're playing with Snapchat's filters - sticking your tongue out, ghoulifying your features, and working out how to get the flower crown to fit exactly on your head - surely you've had a moment where you've wondered what's going on, on a technical level - how Snapchat manages to match your face to the animations?
Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.