How does it work; and more importantly? Essence of ML and Hoeffding’s inequality. Back in 2017, I was introduced to a cool wor(l)d. The word by itself was very intriguing.
Back in 2017, I was introduced to a cool wor(l)d. The word by itself was very intriguing. I was interested and wanted to know more. So as any normal person would, I opened my web browser and typed “What is Machine Learning?
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And this Venn diagram popped up. Along with other words with a common suffix: supervised learning, unsupervised learning, reinforcement learning, deep learning. This was followed by other words with increasing order of complexity: data, classification, regression, models, clusters, ensembles, support vectors, neural networks, etc, etc, etc.
After spending almost a year to try and understand what all those terms meant, converting the knowledge gained into working codes and employing those codes to solve some real-world problems, something important dawned on me.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different
Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.