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

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Towards AI

JULY 22, 2023

This is similar to how machine learning (ML) can seem at first. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from. They guide you, correct you, and provide feedback, helping you learn and improve. But don’t worry!

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Pickl AI

FEBRUARY 17, 2025

This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges. What is Machine Learning? This scalability is crucial for businesses looking to harness the full potential of their data assets.

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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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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Taxonomy of the self-supervised learning Wang et al. 2022’s paper.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Google Research has been at the forefront of this effort, developing many innovations from privacy-safe recommendation systems to scalable solutions for large-scale ML. You can find other posts in the series here.)

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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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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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ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

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Mlearning.ai

FEBRUARY 17, 2023

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

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Pickl AI

JULY 17, 2023

Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning. However, unsupervised learning can be highly unpredictable compared to natural learning methods. It can be either agglomerative or divisive.

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Towards AI

APRIL 4, 2024

Shall we unravel the true meaning of machine learning algorithms and their practicability? Lets look at some of this algorithm and their code snippet with the main platform being google earth engine focusing on supervised learning.

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Explosion

MAY 17, 2023

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

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Snorkel AI

MARCH 30, 2023

Rapid, model-guided iteration with New Studio for all core ML tasks. Enhanced studio experience for all core ML tasks. Prompt LF Builder: Explore and label data through natural language prompts using FM knowledge and translate it into labeling functions for your weakly supervised learning use cases. Advanced SDK tools.

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NYU Center for Data Science

NOVEMBER 15, 2023

Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce Harrop, Benjamin Hillman, Andrea Jenney, Savannah L.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai

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Dataconomy

MAY 25, 2023

ML algorithms for analyzing IoT data using artificial intelligence Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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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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Snorkel AI

FEBRUARY 6, 2023

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. It’s essentially self -supervised learning. We go and search for that, and let’s see what the system is able to find.

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Snorkel AI

FEBRUARY 6, 2023

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. It’s essentially self -supervised learning. We go and search for that, and let’s see what the system is able to find.

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Mlearning.ai

MAY 3, 2023

K-Means Clustering What is K-Means Clustering in Machine Learning? K-Means Clustering is an unsupervised machine learning algorithm used for clustering data points into groups or clusters based on their similarity. How Does K-Means Clustering Work? How is K Determined in K-Means Clustering?

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AWS Machine Learning Blog

MAY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. Observing patterns in the data allows a deep-learning model to cluster inputs appropriately.

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Pickl AI

NOVEMBER 4, 2024

Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python. Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms. It offers simple and efficient tools for data mining and Data Analysis.

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. DL demands high computational power, whereas ML can run on standard systems.

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance. What is Machine Learning?

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Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Google Research AI blog

APRIL 30, 2023

Bellemare Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets Edo Cohen-Karlik , Itamar Menuhin-Gruman , Raja Giryes , Nadav Cohen , Amir Globerson Weighted Ensemble Self-Supervised Learning Yangjun Ruan *, Saurabh Singh , Warren Morningstar , Alexander A.

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Pickl AI

DECEMBER 3, 2024

Established in 1987 at the University of California, Irvine, it has become a global go-to resource for ML practitioners and researchers. The global Machine Learning market continues to expand. The UCI Machine Learning Repository is a well-known online resource that houses vast Machine Learning (ML) research and applications datasets.

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PyImageSearch

MARCH 30, 2023

What is Machine Learning A Closer Look at Machine Learning Machine learning (ML) revolves around creating algorithms and related systems that learn behavior strategies in specific environments using instructions and training data sets.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value.

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Google Research AI blog

JULY 23, 2023

Posted by Cat Armato, Program Manager, Google Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more.

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Becoming Human

MAY 15, 2023

Note : Now write some articles or blogs on the things you have learned because this thing will help you to develop soft skills as well if you want to publish some research paper on AI/ML so this writing habit will help you there for sure. Performance Metrics These are used to evaluate the performance of a machine-learning algorithm.

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AWS Machine Learning Blog

OCTOBER 30, 2024

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval.

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

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