Machine Learning Mastery

OCTOBER 18, 2024

As a data scientist, you probably know how to build machine learning models. But it’s only when you deploy the model that you get a useful machine learning solution. And if you’re looking to learn more about deploying machine learning models, this guide is for you.

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Machine Learning Machine Learning Data Scientist ML 293

Analytics Vidhya

JANUARY 28, 2022

This article was published as a part of the Data Science Blogathon. Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The post Deploying ML Models Using Kubernetes appeared first on Analytics Vidhya.

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ML ML Data Scientist Machine Learning 346

Analytics Vidhya

APRIL 1, 2022

Introduction Jupyter Notebook is a web-based interactive computing platform that many data scientists use for data wrangling, data visualization, and prototyping of their Machine Learning models. The post How to Convert Jupyter Notebook into ML Web App? appeared first on Analytics Vidhya.

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ML ML Data Wrangling Data Scientist 367

Analytics Vidhya

OCTOBER 15, 2021

This article was published as a part of the Data Science Blogathon What is Model Monitoring and why is it required? Machine learning creates static models from historical data. But, once deployed in production, ML models become unreliable and obsolete and degrade with time.

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Data Scientist Machine Learning Machine Learning Data Science 343

KDnuggets

AUGUST 31, 2022

This article provides insights into how leading data scientists are embracing machine learning in their organizations and covers some of the major ML challenges and trends in the enterprise.

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Machine Learning Machine Learning Data Scientist ML 300

Data Science Dojo

APRIL 25, 2023

SQL (Structured Query Language) is an important tool for data scientists. It is a programming language used to manipulate data stored in relational databases. Mastering SQL concepts allows a data scientist to quickly analyze large amounts of data and make decisions based on their findings.

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Data Scientist SQL Machine Learning Machine Learning 370

Analytics Vidhya

JULY 6, 2023

Machine learning (ML) models can be computationally intensive, and training the models can take longer. Data scientists can iterate faster, experiment […] The post RAPIDS: Use GPU to Accelerate ML Models Easily appeared first on Analytics Vidhya.

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ML ML Data Scientist Artificial Intelligence 302

Analytics Vidhya

APRIL 4, 2022

Introduction One of the key challenges in Machine Learning Model is the explainability of the ML Model that we are building. In general, ML Model is a Black Box. As Data scientists, we may understand the algorithm & statistical methods used behind the scene. […].

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ML ML Data Scientist Machine Learning 290

Dataconomy

APRIL 8, 2025

The ML stack is an essential framework for any data scientist or machine learning engineer. With the ability to streamline processes ranging from data preparation to model deployment and monitoring, it enables teams to efficiently convert raw data into actionable insights. What is an ML stack?

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ML ML Machine Learning Machine Learning 91

KDnuggets

AUGUST 30, 2019

Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

Data Scientist 306

Data Scientist ML ML Machine Learning 306

KDnuggets

JANUARY 2, 2020

In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.

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Machine Learning Machine Learning Azure Data Scientist 322

Data Science Dojo

OCTOBER 31, 2024

For data scientists, this shift has opened up a global market of remote data science jobs, with top employers now prioritizing skills that allow remote professionals to thrive. Here’s everything you need to know to land a remote data science job, from advanced role insights to tips on making yourself an unbeatable candidate.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

Data Science Dojo

JULY 5, 2024

Machine learning models are algorithms designed to identify patterns and make predictions or decisions based on data. These models are trained using historical data to recognize underlying patterns and relationships. Once trained, they can be used to make predictions on new, unseen data.

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Machine Learning Machine Learning ML ML 322

Analytics Vidhya

AUGUST 25, 2019

Overview Interpretable machine learning is a critical concept every data scientist should be aware of How can you build interpretable machine learning models? The post Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in Python appeared first on Analytics Vidhya.

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Machine Learning Machine Learning Python Data Scientist 307

Data Science Dojo

MARCH 24, 2023

Ready to revolutionize the way you deploy machine learning? Look no further than ML Ops – the future of ML deployment. Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. What is ML Ops?

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Machine Learning Machine Learning ML ML 195

AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Choose Predict.

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Data Warehouse Machine Learning Machine Learning Cloud Data 114

IBM Data Science in Practice

MARCH 8, 2023

Feature Platforms — A New Paradigm in Machine Learning Operations (MLOps) Operationalizing Machine Learning is Still Hard OpenAI introduced ChatGPT. The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years.

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Machine Learning Machine Learning ML ML 264

Data Science Dojo

FEBRUARY 13, 2025

With rapid advancements in machine learning, generative AI, and big data, 2025 is set to be a landmark year for AI discussions, breakthroughs, and collaborations. This conference brings together industry leaders, data scientists, AI engineers, and business professionals to discuss how AI and big data are transforming industries.

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Big Data Big Data AI AI 294

insideBIGDATA

FEBRUARY 23, 2023

Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work.

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Data Scientist ML ML Big Data 542

Towards AI

NOVEMBER 4, 2024

While traditional opinion polls provide a pretty good snapshot, machine learning certainly goes deeper with its data-driven perspective on things. One fact is that machine learning has begun changing data-driven political analysis. With the end goal of predicting the U.S.

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Machine Learning Machine Learning Exploratory Data Analysis EDA 119

Dataconomy

MAY 16, 2023

Machine learning engineer vs data scientist: two distinct roles with overlapping expertise, each essential in unlocking the power of data-driven insights. As businesses strive to stay competitive and make data-driven decisions, the roles of machine learning engineers and data scientists have gained prominence.

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Data Scientist ML ML Machine Learning 156

Flipboard

DECEMBER 7, 2023

Master Data Science with Top 10 Kaggle ML Projects to become a Data Scientist.

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Data Scientist Machine Learning Machine Learning Data Science 178

AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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AWS ML ML Data Scientist 105

insideBIGDATA

OCTOBER 14, 2023

A recent survey of data scientists and engineers revealed that over half (53.3%) of today’s machine learning (ML) teams are planning on deploying a large language model (LLM) application of their own into production “within the next 12 months” or “as soon as possible”.

Data Scientist 307

Data Scientist Machine Learning Machine Learning ML 307

Flipboard

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. To view this series from the beginning, start with Part 1.

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Data Governance ML ML Data Lakes 132

Analytics Vidhya

NOVEMBER 1, 2021

This article was published as a part of the Data Science Blogathon. Image 1- [link] Whether you are an experienced or an aspiring data scientist, you must have worked on machine learning model development comprising of data cleaning, wrangling, comparing different ML models, training the models on Python Notebooks like Jupyter.

AWS 319

AWS Data Scientist Machine Learning Machine Learning 319

Data Science Dojo

MARCH 24, 2023

Ready to revolutionize the way you deploy machine learning? Look no further than MLOps – the future of ML deployment. Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. What is MLOps?

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Machine Learning Machine Learning ML ML 195

Towards AI

DECEMBER 15, 2024

When it comes to machine learning regression models, interviewers typically focus on five key performance metrics, which are the ones mostly used by Data Scientists in real time. Introduction Model performance metrics are a crucial component of the Machine Learning lifecycle that comes after model training.

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ML ML Data Scientist Machine Learning 116

Analytics Vidhya

FEBRUARY 24, 2021

ArticleVideo Book Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. The post A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional appeared first on Analytics Vidhya.

Data Science 378

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 378

KDnuggets

OCTOBER 24, 2022

TheSequence recently released the first ever ML Chain Landscape shaped by data scientists, a new landscape that would be able to address the entire ML value chain.

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ML ML Data Scientist Machine Learning 217

Data Science Dojo

AUGUST 6, 2024

Their ability to uncover feature importance makes them valuable tools for various ML tasks, including classification, regression, and ranking problems. As a result, boosting algorithms have become a staple in the machine learning toolkit. Boosting algorithms work with these components to enhance ML functionality and accuracy.

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Machine Learning Machine Learning Algorithm ML 195

Data Science Dojo

APRIL 3, 2023

Drag and drop tools have revolutionized the way we approach machine learning (ML) workflows. Gone are the days of manually coding every step of the process – now, with drag-and-drop interfaces, streamlining your ML pipeline has become more accessible and efficient than ever before. How do drag and drop tools work?

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ML ML Machine Learning Machine Learning 195

KDnuggets

DECEMBER 14, 2022

This blog post uses the Concrete-ML library, allowing data scientists to use machine learning models in fully homomorphic encryption (FHE) settings without any prior knowledge of cryptography. We provide a practical tutorial on how to use the library to build a sentiment analysis model on encrypted data.

Data Scientist 271

Data Scientist Machine Learning Machine Learning ML 271

Data Science Dojo

SEPTEMBER 23, 2024

In the world of machine learning, evaluating the performance of a model is just as important as building the model itself. Understand the basics of Binomial Distribution and its importance in ML Interpreting the Key Metrics High Recall : The model is good at identifying actual Spam emails (high Recall of 87.5%).

Machine Learning 434

Machine Learning Machine Learning Data Scientist ML 434

AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. These geospatial capabilities open up a new world of possibilities for environmental monitoring.

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ML ML Clustering Machine Learning 119

Dataconomy

JULY 24, 2023

If you’ve found yourself asking, “How to become a data scientist?” In this detailed guide, we’re going to navigate the exciting realm of data science, a field that blends statistics, technology, and strategic thinking into a powerhouse of innovation and insights. What is a data scientist?

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Data Scientist Data Science Data Analyst Machine Learning 172

AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. In the following example, we show how to fine-tune the latest Meta Llama 3.1

ML 101

ML ML Python AWS 101