Machine Learning Mastery

JANUARY 8, 2025

Data scientists, machine learning practitioners, and AI engineers alike can fall into common training or fine-tuning patterns that could compromise a model’s performance or scalability. Introduction Training large language models (LLMs) is an involved process that requires planning, computational resources, and domain expertise.

Data Scientist 291

Data Scientist Machine Learning Machine Learning AI 291

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

KDnuggets

JUNE 26, 2025

Whats the overall data quality score? Most data scientists spend 15-30 minutes manually exploring each new dataset—loading it into pandas, running.info() ,describe() , and.isnull().sum() sum() , then creating visualizations to understand missing data patterns. Which columns are problematic?

Data Quality 264

Data Quality Data Science Natural Language Processing Machine Learning 264

Analytics Vidhya

DECEMBER 18, 2024

Working with APIs in Google Colab is a common practice for data scientists, researchers, and developers. However, handling API keys, which are essentially passwords granting access to these services, requires careful consideration.

Data Scientist 207

Data Scientist Analytics Analytics Data Analysis 207

Towards AI

NOVEMBER 9, 2024

Author(s): Julia Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Everybody’s talking about AI, but how many of those who claim to be “experts” can actually break down the math behind it?

Data Scientist 119

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Towards AI

NOVEMBER 1, 2024

Automate Data Analysis with Pandas This member-only story is on us. Created with Abidin Dino AI, to reach it, consider being Paid subscriber to LearnAIWithMe, here Pandas is undoubtedly the most powerful data science library, but what if I told you that you could automate data analysis and complete your work with just a click?

Data Scientist 111

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KDnuggets

JUNE 19, 2025

Ready-to-Use Libraries for (Almost) Every Data Task The language offers popular libraries for almost every data task youll work on — from data cleaning, manipulation, visualization, and building machine learning models. We outline must-know data science libraries in 10 Python Libraries Every Data Scientist Should Know.

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Python Natural Language Processing Data Science Machine Learning 280

KDnuggets

JUNE 11, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale Top 7 MCP Clients for AI Tooling Why You Need RAG to Stay Relevant as a Data Scientist Stop Writing Messy Python: A Clean Code Crash Course Selling Your Side Project?

Natural Language Processing 301

Natural Language Processing Data Science Machine Learning Machine Learning 301

Flipboard

JULY 1, 2025

This is a must-have bookmark for any data scientist working with Python, encompassing everything from data analysis and machine learning to web development and automation. Ideal for data scientists and engineers working with databases and complex data models.

Data Science 171

Data Science Machine Learning Machine Learning Natural Language Processing 171

KDnuggets

JUNE 23, 2025

It supports data scientists and engineers working together. It manages the entire machine learning lifecycle. It provides tools to simplify workflows. These tools help develop, deploy, and maintain models. MLflow is great for team collaboration. It keeps track of experiments and results. It packages code for reproducibility.

Machine Learning 224

Machine Learning Machine Learning Natural Language Processing Data Science 224

KDnuggets

JULY 7, 2025

We also did this using a real-life data project that Uber requested in the data scientist recruitment process. For data scientists working on analysis-heavy tasks, it’s a lightweight but powerful alternative to Pandas. Nate Rosidi is a data scientist and in product strategy.

SQL 262

SQL Data Science Natural Language Processing Machine Learning 262

KDnuggets

JULY 15, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Next post => Latest Posts 10 GitHub Repositories for Python Projects Building End-to-End Data Pipelines: From Data Ingestion to Analysis Bootstrapping Your Freelance Data Science Business for Cheap How to Optimize Your Python Code Even If You’re a Beginner (..)

Data Pipeline 258

Data Pipeline Natural Language Processing Data Science SQL 258

Analytics Vidhya

APRIL 28, 2025

For data scientists and AI engineers, it offers a solid framework to spot those hidden prejudices that often slip into language models without notice. When you’re working on building fair and responsible AI, having a way to actually measure bias in your models is key. This is where Bias Score comes to the picture.

Data Scientist 184

Data Scientist Analytics Analytics AI 184

Analytics Vidhya

MAY 23, 2025

Cleaning data used to be a time-consuming and repetitive process, which took up much of the data scientist’s time. But now with AI, the data cleaning process has become quicker, wiser, and more efficient.

Clean Data 181

Clean Data Data Scientist AI AI 181

KDnuggets

JULY 4, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts Serve Machine Learning Models via REST APIs in Under 10 Minutes A Gentle Introduction to Principal Component Analysis (PCA) in Python AI-First Google Colab is All You Need The Velvet Sundown: Unveiling AI’s Center Stage Act 7 Mistakes Data Scientists (..)

Machine Learning 296

Machine Learning Machine Learning Natural Language Processing Data Science 296

ODSC - Open Data Science

JUNE 19, 2025

ODSC East 2025 delivered again, packed with cutting-edge discussions, forward-looking use cases, and some of the most insightful minds in AI and data science. These slides — still publicly available — offer a snapshot of today’s rapidly evolving data landscape, from lightweight LLMs to production-grade agentic applications.

Data Scientist 52

Data Scientist Data Science ML ML 52

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. For a Data Scientist, These metrics are a key part of building models and come up often in daily work. Thank you for reading.

ML 116

ML ML Data Scientist Machine Learning 116

Dataconomy

MARCH 5, 2025

Key capabilities of data science platforms Scalability and flexibility One of the primary features of data science platforms is their ability to scale resources based on project demands. Collaboration features Data science platforms enhance team collaboration during the data modeling process.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

KDnuggets

JUNE 24, 2025

Python for Modern Data Workflows: Need Help Deciding?

Python 253

Python Natural Language Processing Data Science Machine Learning 253

KDnuggets

JULY 8, 2025

You can run this and immediately see your processed data. Wrapping Up This pipeline takes raw transaction data and turns it into something an analyst or data scientist can actually work with. You can find the complete code on GitHub. Youve got clean records, calculated fields, and meaningful segments.

ETL 239

ETL Data Science Python Natural Language Processing 239

KDnuggets

JUNE 27, 2025

This tutorial demonstrates a significant shift in how data scientists can share their work. With just two Python files and a handful of methods, youve built a complete dashboard that rivals expensive business intelligence tools.

Natural Language Processing 253

Natural Language Processing Data Science Machine Learning Machine Learning 253

AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party. It also empowers data scientists and ML engineers to do more with their models by collaborating seamlessly with their colleagues in data and analytics teams.

AWS 135

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KDnuggets

JULY 7, 2025

Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. People are making millions by doing this. So, what are you waiting for?

Natural Language Processing 338

Natural Language Processing Machine Learning Machine Learning Data Science 338

Data Science Dojo

JULY 14, 2025

Staying current with the latest breakthroughs is essential for data scientists, AI engineers, and researchers who want to leverage the full potential of generative AI. Generative AI research is rapidly transforming the landscape of artificial intelligence, driving innovation in large language models, AI agents, and multimodal systems.

Machine Learning 221

Machine Learning Machine Learning AI AI 221

KDnuggets

JULY 16, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Next post => Latest Posts The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis One-Liners 10 GitHub Repositories for Python Projects Building End-to-End Data Pipelines: From Data Ingestion to Analysis (..)

Python 212

Python Natural Language Processing Data Science Machine Learning 212

KDnuggets

JULY 17, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts Build Your Own Simple Data Pipeline with Python and Docker 10 Surprising Things You Can Do with Python’s collections Module The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis (..)

Data Pipeline 256

Data Pipeline Python ETL Natural Language Processing 256

Flipboard

JUNE 12, 2025

Wrapping Up Learning math can definitely help you grow as a data scientist. You should be able to choose between techniques based on their mathematical assumptions, look at an algorithms implementation and understand the math behind it, and the like. This transformation doesnt happen through memorization or academic rigor.

Data Science 164

Data Science Natural Language Processing Hypothesis Testing Machine Learning 164

Analytics Vidhya

JUNE 27, 2025

Businesses around the world are scrambling to integrate machine learning into their functions, and new opportunities for aspiring data scientists are growing multifold. Machine learning is prevalent in most of the mainstream industries of today. A large number of people […] The post What is Machine Learning?

Machine Learning 172

Machine Learning Machine Learning Data Scientist Analytics 172

ODSC - Open Data Science

JULY 11, 2025

From Moneyball’s transformative impact on baseball to real-time player tracking in basketball and football, data-driven decision-making is redefining how games are played, coached, and consumed. Sports data offers several benefits for learning and experimentation. It’s relatable — many data scientists are already passionate fans.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Dataconomy

JUNE 25, 2025

There is a clear distinction between data stewards and data owners, particularly regarding accessing and managing data. Differentiation from data analysts and data scientists Understanding the differences between data stewards and other data roles is key for clear expectations and responsibilities.

Data Governance 91

Data Governance Data Analyst Data Quality Data Scientist 91

DrivenData Labs

JUNE 19, 2025

Building internal capacity : To support long-term success, CodePath launched a hiring initiative for a full-time data engineer and a data scientist. We created a suite of interactive dashboards and reports, allowing staff across departments to get the insights they need on demand and without needing to write code.

Data Scientist 130

Data Scientist Data Warehouse Data Engineering Data Engineering 130

AWS Machine Learning Blog

NOVEMBER 6, 2024

However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users. Streamlit allows data scientists to create interactive web applications using Python, using their existing skills and knowledge.

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AWS Python AI AI 119

AWS Machine Learning Blog

OCTOBER 22, 2024

Extensively used by data scientists and ML engineers across various industries, this robust tool provides high availability and uninterrupted access for its users. The solution illustrated in this post focuses on the new SageMaker Studio experience, particularly private JupyterLab and Code Editor spaces.

AWS 113

AWS ML ML Machine Learning 113

Dataconomy

JULY 7, 2025

Support for data science Data lakes serve as centralized repositories for raw data, simplifying the workflow for data scientists and analysts. This centralization eases the preparation and analysis of data for complex modeling and insight generation.

Data Lakes 91

Data Lakes Data Warehouse Hadoop Analytics 91

Flipboard

JULY 9, 2025

Conclusion Data scientists and machine learning engineers are not the only positions that work in AI. Nate Rosidi is a data scientist and in product strategy. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.

Machine Learning 149

Machine Learning Machine Learning Natural Language Processing Data Science 149

Dataconomy

JULY 9, 2025

Besides being a data scientist, he’s an expert at building machine learning solutions where margins for error are razor-thin. As the first data scientist at Credibility Capital, he developed credit models from scratch. That’s where Hatim Kagalwala comes in.

Machine Learning 91

Machine Learning Machine Learning Data Science Data Scientist 91

Flipboard

JUNE 16, 2025

I’ve worked as a data scientist in FinTech for six years. You don’t need a PhD to be a data scientist or win a ML competition. When I saw that Spectral Finance was running a credit scoring challenge for Web3 wallets, I decided to give it a try despite having zero blockchain experience.

Machine Learning 128

Machine Learning Machine Learning Data Science Artificial Intelligence 128