Artificial Neural Networks, an Introduction
Artificial neural networks help solve problems in data science. They imitate neural networks of the brain. Neurons function as organic[…]
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Artificial Neural Network Disadvantages
Artificial Neural Networks (ANNs) can solve an extremely wide variety of problems, including virtually any problem reduceable to functions. Often[…]
Read moreUnderspecification in AI
Underspecification is the failure to specify in enough detail, according to Wiktionary. In machine learning and AI, the underspecification of[…]
Read moreRacism and Gender Bias in Machine Learning, Fixes not Always Easy
Racism and gender bias can easily and inadvertently infect machine learning algorithms. One prime example examined what job applicants were[…]
Read moreCross Validation and Model Scoring with Sklearn
Machine Learning Tutorial Part III One of the most important aspects of machine learning is choosing the best model. The[…]
Read moreTrain_test_split with Sklearn
Machine Learning Tutorial part II After you evaluate the scores for each column, select the columns with the highest scores.[…]
Read morePredictive Models and Algorithm Choice
Machine learning and predictive modeling require you to choose the right algorithm. Data scientists and data analysts have to train,[…]
Read moreMy Data Analysis Boot Camp Experience
Numerous options are available for people trying to learn about data science. Better options tend to cost money. While self-paced[…]
Read moreAI and Data Analytics are Changing B2B Advertising
A look at how WTWH Media uses AI for Advertising One frequently overlooked kind of data in the advertising realm[…]
Read moreWhy Linear Algebra?
I am in the process of learning data science. During a discussion with my father about what I was learning,[…]
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