Husna Sayedi
Husna Sayedi

Husna is a data scientist and has studied Mathematical Sciences at University of California, Santa Barbara. She also holds her master’s degree in Engineering, Data Science from University of California Riverside. She has experience in machine learning, data analytics, statistics, and big data. She enjoys technical writing when she is not working and is currently responsible for the data science-related content at TAUS.

A thorough overview of the paper by six Google researchers: Data Cascades in High-Stakes AI with a focus on why data-centric AI matters.
Explaining what Explainable AI (XAI) entails and diving into five major XAI techniques for Natural Language Processing (NLP).
A brief definition of what training data is.
Reasons why training data is important for AI and ML practices.
A brief introduction to types of training data including structured, unstructured, and semi-structured data.
Here are some pointers on how much training data do you need to train your ML models.
Data cleaning and data anonymization are very critical in training ML models. Here are the reasons why.
Training data can be sourced via synthetic data generation, public datasets, data marketplaces, and crowd-sourced platforms.
Definition and common use cases of intent recognition as an essential element of NLP.
Understanding the popular subfield of NLP known as sentiment analysis in ML and AI including sentiment analysis definition, types and use cases.
Data preparation techniques for your machine learning (ML) model to yield better predictive power.
Overview of types of machine learning and tips on selecting the right ML model for your AI applications.
What is image annotation and what are some image annotation tips you can use in your AI and ML projects?
Comparing synthetic data vs organic data in machine learning (ML) for an artificial intelligence (AI) application.
Three key NLP tips on how to process text data for an Artificial Intelligence (AI) application, including pre-processing, feature extraction, and model selection.
Data labeling is an integral step in data preparation and pre-processing for training AI and ML systems. Here is a detailed look into what it means and various data labeling techniques.
How to improve the quality of your data through enhancing data integrity, data cleaning, and data modeling to advance your data science and AI (artificial intelligence) practices.