ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). In this comprehensive guide, I will demystify machine learning, breaking it down into digestible concepts for beginners. The goal is to learn a mapping between the inputs and the corresponding outputs.

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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Also, learn about common algorithms used in machine learning.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Mlearning.ai

DECEMBER 13, 2023

Mastering Deep Learning and AI Interview Questions: What You Need to Know Image created by the author on Canva Knowledge is power, but enthusiasm pulls the switch.” Ever wondered what it takes to excel in deep learning interviews? Explain how you would implement transfer learning in a deep learning model.

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Dataconomy

MAY 22, 2023

There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model learns from labeled examples, where the input data is paired with corresponding target labels.

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Google Research AI blog

APRIL 27, 2023

That is where we can use the ability of ML models to pick up on subtle intricate patterns in large amounts of data. We’ve previously demonstrated the ability to use ML models to quickly phenotype at scale for retinal diseases. We trained ML models to predict whether an individual has COPD using the full spirograms as inputs.

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ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Everybody knows you need to clean your data to get good ML performance. A common gripe I hear is: “Garbage in, garbage out.

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Heartbeat

FEBRUARY 28, 2023

The past few years have witnessed exponential growth in medical image analysis using deep learning. In this article we will look into medical image segmentation and see how deep learning can be helpful in these cases. This can be further classified as supervised and unsupervised learning. Image by author.

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Data Science Dojo

JUNE 1, 2023

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. Fundamental Programming Skills Strong programming skills are essential for success in ML.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Towards AI

APRIL 7, 2024

Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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Heartbeat

FEBRUARY 15, 2023

It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. plot(history) Make sure you log the training loss and accuracy metrics to Comet ML.

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phData

AUGUST 24, 2023

What Does a Credit Score or Decisioning ML Pipeline Look Like? Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models. Let’s take a brief look at the below image to see how Snowpark can be used for an end-to-end machine learning solution.

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Pickl AI

OCTOBER 9, 2023

This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming. Unsupervised learning algorithms Unsupervised learning algorithms are a vital part of Machine Learning, used to uncover patterns and insights from unlabeled data.

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Pickl AI

OCTOBER 20, 2024

Step-by-Step Guide to Learning AI in 2024 Learning AI can seem daunting at first, but by following a structured approach, you can build a solid foundation and gain the skills needed to thrive in this field. This step-by-step guide will take you through the critical stages of learning AI from scratch. Let’s dive in!

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Dataconomy

MAY 4, 2023

Machine learning applications in healthcare are revolutionizing the way we approach disease prevention and treatment Machine learning is broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

JULY 10, 2024

Machine Learning (ML) is a subset of AI that involves using statistical techniques to enable machines to improve their performance on tasks through experience. On the other hand, ML focuses specifically on developing algorithms that allow machines to learn and make predictions or decisions based on data.

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Dataconomy

APRIL 18, 2023

Undetectable backdoors can be implemented in any ML algorithm Machine learning Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and make predictions or decisions.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. We partnered with the AI/ML experts at the Amazon ML Solutions Lab for a 14-week development effort.

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Snorkel AI

FEBRUARY 6, 2023

You could imagine, for deep learning, you need, really, a lot of examples. So, deep learning, similarity search is a very easy, simple, task. And that’s the power of self-supervised learning. But desert, ocean, desert, in this way, I think that’s what the power of self-supervised learning is.

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Snorkel AI

FEBRUARY 6, 2023

You could imagine, for deep learning, you need, really, a lot of examples. So, deep learning, similarity search is a very easy, simple, task. And that’s the power of self-supervised learning. But desert, ocean, desert, in this way, I think that’s what the power of self-supervised learning is.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

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Pickl AI

MARCH 6, 2024

It’s revolutionizing various industries, from healthcare to finance, by enabling computers to learn from large datasets and make predictions or decisions based on that data. ML interview questions for freshers Machine Learning engineering interview questions for freshers 1. What is Machine Learning?

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Heartbeat

MARCH 7, 2024

If you’re familiar with machine learning and statistical modeling, you know Sklearn is a powerful tool that provides users with various unsupervised and supervised learning algorithms for building robust machine learning models. Something else I found interesting was that this library maintains scikit-learn’s workflow.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. A few AI technologies are empowering drug design.

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AssemblyAI

SEPTEMBER 12, 2023

Though once the industry standard, accuracy of these classical models had plateaued in recent years, opening the door for new approaches powered by advanced Deep Learning technology that’s also been behind the progress in other fields such as self-driving cars. The data does not need to be force-aligned.

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Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). In this article, you will learn about object detection through the SWIN Transformer. What is the Swin Transformer? If you’d like to contribute, head on over to our call for contributors.

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. Learn more from Uber’s Olcay Cirit. One of the areas that has seen significant growth is language modeling.

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Pickl AI

SEPTEMBER 23, 2024

Machine Learning Machine Learning (ML) is a crucial component of Data Science. It enables computers to learn from data without explicit programming. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets.

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Pickl AI

SEPTEMBER 17, 2024

What Is the Difference Between Artificial Intelligence, Machine Learning, And Deep Learning? Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

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ODSC - Open Data Science

MAY 1, 2023

Given they’re built on deep learning models, LLMs require extraordinary amounts of data. MLOps can help organizations manage this plethora of data with ease, such as with data preparation (cleaning, transforming, and formatting), and data labeling, especially for supervised learning approaches.

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