Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? What is machine learning?

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Flipboard

JULY 31, 2023

In this post, we’ll show you the datasets you can use to build your machine learning projects. After you create a free account, you’ll have access to the best machine learning datasets. Importance and Role of Datasets in Machine Learning Data is king.

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phData

AUGUST 24, 2023

Credit scoring and decisioning models have been used by financial institutions for many years to predict the risk associated with lending to individuals or entities. However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning.

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How to Learn Machine Learning

JANUARY 21, 2023

Additionally, you’ll need to create a data model that can be used to store user data and process requests. It is important to have a good understanding of the fundamentals of natural language processing (NLP) and machine learning in order to achieve the desired results.

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Heartbeat

MARCH 8, 2023

Text Categorization Text categorization is a machine-learning approach that divides the text into specific categories based on its content. R has a rich set of libraries and tools for machine learning and natural language processing, making it well-suited for spam detection tasks.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

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