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How To Improve Machine Learning Model Accuracy

DagsHub

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. These are: 1.

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. This approach involves techniques where the machine learns from massive amounts of data.

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Embeddings in Machine Learning

Mlearning.ai

Netflix-style if-you-like-these-movies-you’ll-like-this-one-too) All kinds of search Text search (like Google Search) Image search (like Google Reverse Image Search) Chatbots and question-answering systems Data preprocessing (preparing data to be fed into a machine learning model) One-shot/zero-shot learning (i.e.

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Data-driven Attribution Modeling

Data Science Blog

Algorithmic Attribution using binary Classifier and (causal) Machine Learning While customer journey data often suffices for evaluating channel contributions and strategy formulation, it may not always be comprehensive enough. Moreover, random forest models as well as support vector machines (SVMs) are also frequently applied.

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Are AI technologies ready for the real world?

Dataconomy

AI has made significant contributions to various aspects of our lives in the last five years ( Image credit ) How do AI technologies learn from the data we provide? AI technologies learn from the data we provide through a structured process known as training. Another form of machine learning algorithm is known as unsupervised learning.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

SOTA (state-of-the-art) in machine learning refers to the best performance achieved by a model or system on a given benchmark dataset or task at a specific point in time. The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. Citation: Article from IBM archives 2.