Remove Hadoop Remove Natural Language Processing Remove Support Vector Machines
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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. Python is the most common programming language used in machine learning.

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What Does the Modern Data Scientist Look Like? Insights from 30,000 Job Descriptions

ODSC - Open Data Science

Natural Language Processing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. Deep Learning Deep Learning is a specialised subset of Machine Learning involving multi-layered neural networks to solve complex problems. They are handy for high-dimensional data.

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Best Resources for Kids to learn Data Science with Python

Pickl AI

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. It can be easily ported to multiple platforms. To obtain practical expertise, run the algorithms on datasets.