Gain more insight from our expert data scientists on how to use Data Science, Machine Learning, and AI to minimize risk and maximize ROI.
In this blog, we explore techniques of using semantic parsing in combination with syntactic mapping to create a domain-independent methodology of transforming natural language queries to structured query language.
The dynamic CLV calculation takes each client’s background information, product usage, historical complaint, and billing data as input, and generates a unique predicted customer lifetime value as output
Create an effective business dashboard from four options: Strategic Dashboards, Analytical Dashboards, Tactical Dashboards...
Establishing and developing a hybrid data science / IT team is an important step in moving an organization to AI maturity.
Generally, a data science team is composed of Data Scientists, Data Engineers, Machine Learning Engineers, and Data Analysts...
What is Python Pandas Data Frame? Depending on your Data Set it may make more sense to use Apache Spark. Today we compare use cases for Pandas and Spark.
Indellient Data Scientists attend ICML 2020 bringing important knowledge and studies on NLP for Good and Reinforcement Learning for Climate control...
Using Machine Learning and Data Science can help your company detect fraud and asses risk. 4 steps on how to build a Fraud Detection System with your data.
How to use Natural Language Processing techniques and Machine Learning algorithms to counter fraud in various business industries like banking and insurance
Sample applications of machine learning models include prediction, image/speech recognition, recommendation engine, and fraud detection.
Data Science Trends for 2020, Indellient sees some big predictions on the horizon: Model Interpretability, the rise of Serverless Service, and more...
Indellient Data Scientists, Meina and Jayeeta give a run down on their expirience at Women Who Code NYC 2019. Workshops and Panels all about women in tech.