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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

Humans have been trying to make machines chat for decades. But despite years of research and innovation, their unnatural responses remind us that no, we’re not yet at the HAL 9000-level of speech sophistication. Keep reading to learn: What problems NLP can help solve. Specifics of data used in NLP. Main NLP use cases.

Process 139
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Top 10 Azure Project Ideas for 2023 [Beginners to Advanced]

Knowledge Hut

As cloud enthusiasts, many of us would want to learn it depending on our job roles in the industry. However, a good way of learning the same is to build a project and understand real-world use cases. Top Azure Project Ideas for Beginners 1. Why Microsoft Azure is Important Today?

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How to Build an End to End Machine Learning Pipeline?

ProjectPro

What is a Machine Learning Pipeline? A machine learning pipeline helps automate machine learning workflows by processing and integrating data sets into a model, which can then be evaluated and delivered. A well-built pipeline helps in the flexibility of the model implementation.

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Of Muffins and Machine Learning Models

Cloudera

classification problem. In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? blueberry spacing) is a measure of the model’s interpretability.

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Data Labeling in Machine Learning: Process, Types, and Best Practices

Knowledge Hut

These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. If you are new to this domain and wanted to learn how to label data for machine learning problems, then you’ve landed on the right page. What is Data Labeling for Machine Learning?

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How to do Anomaly Detection using Machine Learning in Python?

ProjectPro

The modeling follows from the data distribution learned by the statistical or neural model. They will only go outside of these expected patterns in exceptional cases, which are usually erroneous or fraudulent. When these exceptional cases occur, they cause something that is called an “anomaly” in the data.

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Concurrently Train Multiple Time Series Models Over Spark with XGBoost

Towards Data Science

Take advantage of the distributive power of Apache Spark and concurrently train thousands of auto-regressive time-series models on big data Photo by Ricardo Gomez Angel on Unsplash 1. I believe that this is quite a common task for many data scientists and machine learning engineers working with SaaS or retail customer data.