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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Towards AI

MARCH 4, 2023

Over multiple articles, we will be discussing the key highlights from the paper and learn why Prompting is considered to be “The Second Sea Change in NLP”. Fully-Supervised Learning (Non-Neural Network) — powered by — Feature Engineering Supervised learning required input-output examples to train the model.

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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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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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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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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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phData

AUGUST 24, 2023

However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning. What Does a Credit Score or Decisioning ML Pipeline Look Like? Say we are part of an ML team working on a decisioning model. Want to learn more?

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Dataconomy

APRIL 18, 2023

In this article, we’ll explore some of the fundamental concepts in artificial intelligence, from supervised and unsupervised learning to bias and fairness in AI. Machine learning techniques can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

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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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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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Towards AI

MARCH 1, 2023

This article features an interview with Professor David Gunkel, discussing the issue of what rights robots, including AI chatbots, should have. MIT course on Introduction to Data-Centric AI This is a practical course on Data-Centric AI, focusing on the impactful aspects of real-world ML applications.

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Becoming Human

MARCH 16, 2023

In this article, we will explore how AI drug discovery is changing the industry. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. ML solutions encompass a diverse array of branches, each with its own unique characteristics and methodologies.

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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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ODSC - Open Data Science

MAY 1, 2023

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. MLOps are also helpful with root cause analysis, performance monitoring, and governance & compliance.

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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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How to Learn Machine Learning

SEPTEMBER 6, 2024

In this article we will explore how AI for Cybersecurity is being used, its main benefits, challenges, and most common use cases, so sit back, relax, and enjoy! The security team defines the criteria, and if cyber attacks hit the mark, AI ML services automate the response and keep the impacted assets isolated. Hello dear reader!

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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

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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Dataconomy

MAY 25, 2023

In the subsequent sections of this article, we will explore the challenges and limitations associated with artificial intelligence in IoT, as well as the key technologies and techniques driving this convergence. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Becoming Human

MAY 15, 2023

You have to learn only those parts of technology that are useful in data science as well as help you land a job. Don’t worry; you have landed at the right place; in this article, I will give you a crystal clear roadmap to learning data science. Because this is the only effective way to learn Data Analysis.

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Chatbots Life

MAY 12, 2023

There are many articles describing the possible use cases of ChatGPT, however, they rarely go into the details about how the model works or discuss its border implications. The second part of the article discusses the possible use cases of ChatGPT and their impact on respected industries. Will ChatGPT replace ML Engineers?

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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. This blog post will clarify some of the ambiguity. It can ingest unstructured data in its raw form (e.g.,

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Snorkel AI

APRIL 18, 2023

We started with this Subset Semantic Scholar Open Research Corpus (S2ORC), which consists of 600K climate-related articles. The goal is to figure out how we can extract the scientific articles that talk about different types of climate hazards. First and foremost, we are faced with this huge corpus of scientific articles.

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Snorkel AI

APRIL 18, 2023

We started with this Subset Semantic Scholar Open Research Corpus (S2ORC), which consists of 600K climate-related articles. The goal is to figure out how we can extract the scientific articles that talk about different types of climate hazards. First and foremost, we are faced with this huge corpus of scientific articles.

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Mlearning.ai

FEBRUARY 23, 2023

In this article, we’ll explore what random forests are, why they’re practical, and how to use them. Since random forests are a subset of supervised learning algorithms, they depend on labeled data. Because they use labeled data, random forests are supervised learning methods. What are Random Forests?

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How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. Data Labeling Accurate labeling is extremely important in supervised learning.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. But first, let’s start from the bottom and better understand where we are now in the age of AI.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. But first, let’s start from the bottom and better understand where we are now in the age of AI.

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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. Less accurate and trustworthy method.

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Pickl AI

JULY 10, 2024

In this fast-evolving field, continuous learning and upskilling are crucial for staying relevant and competitive. This article aims to guide readers in selecting the best AI and Machine Learning Courses to enhance their careers. Despite these differences, AI and ML share several similarities.

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Mlearning.ai

OCTOBER 2, 2023

We’ll talk about supervised and unsupervised feature selection techniques. Learn how to use them to avoid the biggest scare in ML: overfitting and underfitting. We’ll answer exactly that question in this article. The two main categories of feature selection are supervised and unsupervised machine learning techniques.

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Pickl AI

AUGUST 7, 2023

Data Labelling is the process of adding meaning to different datasets ensuring that it can be used properly to train a Machine Learning model. Labeled data in Machine Learning is typically used in the case of Supervised Learning where the labeled data is input to a model. How does Data Labelling Work?

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Mlearning.ai

MAY 12, 2023

Support Vector Machine: A Comprehensive Guide — Part1 Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. I will cover only the first 5 subtopics in this article and will cover the rest in my next upcoming article. Thanks for reading this article!

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Mlearning.ai

AUGUST 6, 2023

What led to this article? I figured, what better way to multiply my impact, than to write articles that can be accessed by people all around the world! Towards the end of my studies, I incorporated basic supervised learning into my thesis and picked up Python programming at the same time. My Story [2.1]

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IBM Journey to AI blog

JULY 6, 2023

What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.

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Google Research AI blog

JANUARY 18, 2023

Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series. Language Models The progress on larger and more powerful language models has been one of the most exciting areas of machine learning (ML) research over the last decade.

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The MLOps Blog

FEBRUARY 22, 2024

At the beginning of my machine learning journey, I was convinced that creating an ML model always looks similar. But most real-world machine learning (ML) projects are not like that. These problems keep many ML practitioners awake at night. If you’re part of this group, continual learning is exactly what you need.

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