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Is Traditional Machine Learning Still Relevant?

Unite.AI

In recent years, Generative AI has shown promising results in solving complex AI tasks. Modern AI models like ChatGPT , Bard , LLaMA , DALL-E.3 Moreover, Multimodal AI techniques have emerged, capable of processing multiple data modalities, i.e., text, images, audio, and videos simultaneously. What are its Limitations?

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4 Key Steps in Preprocessing Data for Machine Learning

Aiiot Talk

Just as a strong foundation ensures the durability and safety of a home, effective preprocessing ensures the success of artificial intelligence (AI) projects. This crucial step involves cleaning and organizing your data and preparing it for your machine-learning models. Think of it as prepping ingredients before cooking.

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Future of AI and Machine Learning in Cybersecurity

Analytics Vidhya

Introduction Artificial Intelligence (AI) and Machine Learning (ML) have rapidly become some of the most important technologies in the field of cybersecurity. With the increasing amount of data and sophisticated cyber threats, AI and ML are being used to strengthen the security of organizations and individuals.

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Top Courses for Machine Learning with Python

Marktechpost

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. and evaluating the same.

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How to establish lineage transparency for your machine learning initiatives

IBM Journey to AI blog

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes.

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Researchers at Microsoft AI Propose LLM-ABR: A Machine Learning System that Utilizes LLMs to Design Adaptive Bitrate (ABR) Algorithms

Marktechpost

One recent application consists of developing heuristic greedy algorithms for NP-hard problems and creating reward functions for robotics use. Also, researchers use the power of LLMs to develop innovative networking algorithms. Still, it is challenging for LLMs to rank these algorithms and pick the best one.

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Top Machine Learning Courses for Finance

Marktechpost

Machine learning is widely applied in finance for tasks like credit scoring, fraud detection, and trading. This course recommends top machine learning courses for finance professionals aiming to harness these techniques for better decision-making and performance.