The Best Way to Practice Data Science Tools. Data science is an interdisciplinary field. Math, statistics, and software skills are required to create a data science product.
Data science is an interdisciplinary field. Math, statistics, and software skills are required to create a data science product. On top of these tools, you need to have analytical thinking skills and domain knowledge to be able to come up with ideas to create value out of data.
In such a broad field, you need both theoretical knowledge and practical skills to do outstanding work. Although some companies divide roles in the pipeline of creating products, the role of data scientists should cover the entire pipeline to some extent.
In this post, we will focus on the practical side and how to improve your skillset in that manner.
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.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.