Relationship between AI, Machine Learning, Deep Learning & Data Science?
We all hear these terms being thrown around and often used interchangeably; some of us tag along without knowing what they mean, or we might see them as buzzwords, and others claim to know — and do — what these terms really entail.
Note that the distinctions between these terms aren’t clear-cut, but this will give a sense of the general uses of the terms, how they are related to one another, and how all are threaded together by data science.
Here is a pictorial representation of the same:
Artificial Intelligence describes machines that can perform tasks resembling those of humans. So AI implies machines that artificially model human intelligence. AI systems help us manage, model, and analyze complex systems. It is the superset which has Machine Learning & Deep Learning as subset.
Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned.
Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence.
Data science is a broad field that spans the collection, management, analysis and interpretation of large amounts of data with a wide range of applications. It integrates all the terms above and summarizes or extract insights from data (exploratory data analysis) and make predictions from large datasets (predictive analytics). The field involves many different disciplines and tools, including statistical inference, domain knowledge (expertise), data visualization, experiment design, and communication. Data science helps answer the question “what if?” and it plays a crucial role in building ML and AI systems, and vice versa.
In fact Data Science & Machine Learning are the stepping stone to the world of #Ai . To learn DS & ML Here are the basics steps you need to follow: Learn Python>Know the Practical usage of Data Science > Stats & Probabilities > Implement it using Python > Data Visualization > Data Analysis > Machine learning.
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