Delve into the World of Vector Databases Fueling NLP's Transformative Journey.

Learn how the differential privacy works by simulating attack on data protected with that technique.

Learn how to measure the quality of word and sentence embeddings in natural language processing (NLP), including intrinsic and extrinsic evaluation, and their strengths and limitations.

We often associate eloquent speech with intelligence and knowledge. But what if I told you that this assumption is not always true?

Want to know why your AI model made that decision? ELI5 has got you covered. Let's dive into Explainable AI with ELI5.

Don't let misleading metrics fool you. Master the art of analyzing regression model performance and make smarter decisions.

EDA Made Easy - Discover Top-10 Python Libraries That Will Take Your Data Analysis to the Next Level! Learn the Secrets of Automated EDA!

Ready to take your Kaggle competition game to the next level? Learn how to recognize and prevent overfitting for top-notch results.

Exploring the new cognitive skills of tomorrow with advanced AI generative models.

Understand the effects of search engines and AI on our mental and cognitive capabilities. Equip yourself with the knowledge you need to make informed decisions about your own usage of these technologies.

Explore the benefits and challenges of using Large Language Models (LLMs) in corporate environments for improved knowledge management. Learn how to implement LLMs and overcome potential obstacles.

Learn how Databricks can help you master big data, improve data processing and machine learning skills and excel in your career. Boost your career with this powerful platform.

Learn about common types of data science projects and best practices for approaching them. From end-to-end individual work to production-ready projects, this guide covers it all.

Discover the latest methods for distinguishing machine-generated text from the human-written text. Learn about statistical, syntactic, semantic, and neural network-based approaches. Stay up-to-date with the latest research in NLP and AI.

This post discusses importance of visual text exploration in preprocessing for classification, covers techniques (wordcloud, Sentiment Analysis, topic modeling, data cleaning) & how to use them with popular libraries. Encourages readers to try for own projects.

Looking for the key to unlocking valuable datasets? Dive into the world of Kaggle, UCI, and more as we unveil the best platforms for data enthusiasts. 🗝️

Unlock the power of document classification with these top Python libraries! Discover the best tools for effortless text analysis and more.

Improve your regression model's accuracy and predictability by uncovering hidden errors with these essential plots.

Sometimes input for document processing tasks such as OCR, table detection or text segmentation can be scanned or photo taken from hand that do not have ideal perspective - is rotated or spatially distorted in some way (warped document). If you are looking for my recommendations go straight to the last section of this article "Summary and recommendations".

Learn about micro and macro averages in multiclass multilabel problems, the difference between multiclass and multilabel problems and when to use micro and macro averages.

Want to create beautiful visualizations from complex data? Discover the power of T-SNE for dimensionality reduction in Python.

Statistics can be tricky, but understanding kurtosis is a must for anyone who wants to avoid making common mistakes in statistical analyses. Learn how to interpret it in this comprehensive guide.

Explore methods to detect & fix errors in data, including validation, visualizations, statistical tests, cleaning techniques, machine learning & data quality tools. Get concise, easy to understand information with examples & links to external resources.

Overview of the available tools and methods for schema validation in pandas, examplary code snippets and recommendation for when to use given tool.

Learn about metrics used to compare histograms with examples of how to calculate them in python. From Chi-Squared distance to Kullback-Leibler divergence and Earth Mover's distance. A comprehensive guide.

This post describe evaluation metrics used in Kaggle competitions where problem to solve is has regression nature. Eight different metrics are described, namely: Absolute Error (AE), Mean Absolute Error (MAE), Weighted Mean Absolute Error (WMAE), Pearson Correlation Coefficient, Spearman’s Rank Correlation, Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE), Mean Columnwise Root Mean Squared Error (MCRMSE).

Exploratory Data Analysis of the Kaggle's "What's cooking" competition dataset to get understanding what kind of data we are dealing with and get intuition of existing dependencies.