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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models.

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Text Classification using Watson NLP

IBM Data Science in Practice

Leverage the Watson NLP library to build the best classification models by combining the power of classic ML, Deep Learning, and Transformed based models. In this blog, you will walk through the steps of building several ML and Deep learning-based models using the Watson NLP library. So, let’s get started with this.

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Are you familiar with the teacher of machine learning?

Dataconomy

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices.

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Journeying into the realms of ML engineers and data scientists

Dataconomy

A machine learning engineer focuses on implementing and deploying machine learning models into production systems. They possess strong programming and engineering skills to develop scalable and efficient machine learning solutions.

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LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

The scope of LLMOps within machine learning projects can vary widely, tailored to the specific needs of each project. Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Model fine-tuning Model training: Once the data is prepared, the LLM is trained.

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Mastering Large Language Models: PART 1

Mlearning.ai

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape.

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Get Maximum Value from Your Visual Data

DataRobot

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. it’s possible to build a robust image recognition algorithm with high accuracy. We embedded best practices and various deep learning models to support image data. Submit Data.