ODSC - Open Data Science

SEPTEMBER 19, 2023

They work closely with a multidisciplinary team that includes other engineers, data scientists, and product managers. Depending on the position, and company, it can require a strong understanding of natural language processing, computer science, linguistics, and software engineering.

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). His specialty is Natural Language Processing (NLP) and is passionate about deep learning.

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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.

Machine Learning 110

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Women in Big Data

DECEMBER 13, 2023

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Computational Linguistics is rule based modeling of natural languages. The event was part of the chapter’s technical talk series 2023.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy.

AI 70

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Pickl AI

SEPTEMBER 16, 2024

However, there are certain algorithms that have stood the test of time and remain crucial for any data scientist or Machine Learning practitioner to understand. This section will explore the top 10 Machine Learning algorithms that you should know in 2024. Applications Image Recognition: Identifying objects in images.

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Dataconomy

MARCH 27, 2023

Some of the ways in which ML can be used in process automation include the following: Predictive analytics:  ML algorithms can be used to predict future outcomes based on historical data, enabling organizations to make better decisions. Technology: Includes a range of technologies, including ML and deep learning.

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AWS Machine Learning Blog

MAY 8, 2024

Amazon SageMaker JumpStart 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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Pickl AI

JULY 12, 2024

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. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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Data Science 101

OCTOBER 31, 2019

The former is a term used for models where the data has been labeled, whereas, unsupervised learning, on the other hand, refers to unlabeled data. Classification is a form of supervised learning technique where a known structure is generalized for distinguishing instances in new data. Classification.

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IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. Generative AI relies on foundation models to create a scalable process.

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Flipboard

DECEMBER 6, 2023

Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process. Suhas chowdary Jonnalagadda is a Data Scientist at AWS Global Services. These models are larger in parameter size and perform better in tasks.

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IBM Journey to AI blog

JULY 6, 2023

Natural language processing (NLP) and computer vision, which let companies automate tasks and underpin chatbots and virtual assistants such as Siri and Alexa, are examples of ANI. It can ingest unstructured data in its raw form (e.g., It can ingest unstructured data in its raw form (e.g.,

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

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

It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed. Challenges of data science Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day.

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Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

SEPTEMBER 18, 2024

Tech companies, they might focus on developing recommendation systems, fraud detection algorithms, or Natural Language Processing tools. Their work environments are typically collaborative, involving teamwork with Data Scientists, software engineers, and product managers. Platforms like Pickl.AI

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Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

Data Science 52

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ODSC - Open Data Science

NOVEMBER 8, 2023

Over the past year, new terms, developments, algorithms, tools, and frameworks have emerged to help data scientists and those working with AI develop whatever they desire. There’s a lot to learn for those looking to take a deeper dive into generative AI and actually develop those tools that others will use.

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AI AI Natural Language Processing Data Science 52

Heartbeat

MARCH 7, 2024

ScikitLLM is interesting because it seamlessly integrates LLMs into your traditional Scikit-learn (Sklearn) library. This means Scikit-LLM brings the power of powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks. Also, do check out the logged model on the Comet platform.

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Machine Learning Machine Learning ML ML 52

Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Pickl AI

OCTOBER 20, 2024

AI-related roles, such as Machine Learning Engineers, Data Scientists, and AI Developers, are in high demand. Diverse career paths : AI spans various fields, including robotics, Natural Language Processing , computer vision, and automation. Deep Learning is a subset of ML. Lakhs to ₹23.4

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Pickl AI

AUGUST 1, 2024

Unlike traditional Machine Learning, which often relies on feature extraction and simpler models, Deep Learning utilises multi-layered neural networks to automatically learn features from raw data. Insufficient or low-quality data can lead to poor model performance and overfitting.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. Large language models (LLMs) have taken the field of AI by storm.

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Clustering AI AI Data Science 95

Heartbeat

OCTOBER 11, 2023

Instead of memorizing the training data, the objective is to create models that precisely predict unobserved instances. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. We pay our contributors, and we don't sell ads.

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Pickl AI

APRIL 2, 2024

Building Your Data Science Team Data science talent is in high demand. Here are some options to consider: Hire Data Scientists: This is ideal for complex projects requiring expertise in specific areas. Upskill Existing Employees: Train employees with analytical skills in data science fundamentals.

Data Science 52

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ODSC - Open Data Science

FEBRUARY 14, 2023

ChatGPT is a next-generation language model (referred to as GPT-3.5) Some examples of large language models include GPT (Generative Pre-training Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly Optimized BERT Approach).

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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. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. predicting house prices).

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

JULY 10, 2024

Inspired by the human brain, neural networks are crucial for deep learning, a subset of ML that deals with large, complex datasets. Natural Language Processing (NLP) allows machines to understand and generate human language, enhancing interactions between humans and machines. Focus on career-essential soft skills.

Machine Learning 52

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Heartbeat

JULY 6, 2023

At a high level, the Swin Transformer is based on the transformer architecture, which was originally developed for natural language processing but has since been adapted for computer vision tasks. The Swin Transformer is part of a larger trend in deep learning towards attention-based models and self-supervised learning.

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PyImageSearch

MARCH 30, 2023

Machine learning encompasses several strategies that teach algorithms to recognize patterns in data, guiding informed actions in similar settings. These strategies include: Supervised Learning: In this approach, data scientists provide ML systems with training data sets containing inputs and corresponding desired outputs.

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ODSC - Open Data Science

OCTOBER 15, 2023

Elementl’s platform is designed for data engineers, while Dagster Labs’ platform is designed for data scientists. Elementl’s platform is also more focused on managing data pipelines, while Dagster Labs’ platform is more focused on building data pipelines.

Machine Learning 52

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AWS Machine Learning Blog

AUGUST 7, 2023

AWS received about 100 samples of labeled data from the customer, which is a lot less than the 1,000 samples recommended for fine-tuning an LLM in the data science community. Improving Language Understanding by Generative Pre-Training” Devlin et al., Safa Tinaztepe is a full-stack data scientist with AWS Professional Services.

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Heartbeat

FEBRUARY 15, 2023

Source: [link] Text classification is an interesting application of natural language processing. It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. These algorithms can perform sentiment analysis, create spam filters, and other applications.

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Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

Machine Learning 52

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Pickl AI

AUGUST 1, 2023

Text mining is also known as text analytics or Natural Language Processing (NLP). It is the process of deriving valuable patterns, trends, and insights from unstructured textual data. Visualize the sentiment distribution and analyze trends and patterns in the data. Wrapping it up !!!

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