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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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Your Ultimate Guide to Coursera Machine Learning Top Courses

How to Learn Machine Learning

Course Highlights: Detailed exploration of supervised and unsupervised learning In-depth coverage of linear regression, logistic regression, and neural networks Advanced topics including support vector machines and anomaly detection Practical implementation using MATLAB/Octave Insights into machine learning best practices and optimization techniques (..)

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

Some common supervised learning algorithms include decision trees, random forests, support vector machines, and linear regression. These algorithms help businesses make decisions when there is clear historical data available. Unsupervised learning uses algorithms that help discover groupings and associations in data.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

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AI Drug Discovery: How It’s Changing the Game

Becoming Human

These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines. A combination of factors is driving this trend.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Journey to AI blog

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

Pickl AI

Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. Gather a dataset of customer support tickets with different categories, such as billing, technical issues, or product inquiries. What is a text mining algorithm?