Brendan is the senior curriculum developer for data science at the Flatiron School. He holds degrees in mathematics, data science, and philosophy, and enjoys modeling neural networks with the Python library TensorFlow.
More from Brendan
Artificial Intelligence
Demystifying Machine Learning: What AI Can Do for You
By demystifying machine learning, we unveil a world of possibilities where AI becomes not just a buzzword, but a tangible tool for enhancing productivity, efficiency, and innovation.
Tech Trends
Intro to Predictive Modeling: A Guide to Building Your First Machine Learning Model
Predictive modeling in data science involves using statistical algorithms and machine learning techniques to build models that predict future outcomes or behaviors based on historical data. It encompasses various steps, including data preprocessing, feature selection, model training, evaluation, and deployment.
Tech Trends
Introduction to Natural Language Processing (NLP) in Data Science
Natural Language Processing (NLP) encompasses a variety of techniques designed to enable computers to understand and process human languages. In this post you’ll learn about NLP applications like text classification and sentiment analysis, plus NLP techniques like tokenization and stemming.
Tech Trends
Decoding Data Jargon: Your Key to Understanding Data Science Terms
You should only use data science terms such as mean, median, standard deviation, correlation, and hypothesis testing if you are confident in being able to explain them. This post walks readers through explanations of some of the most common data science terms.
Tech Trends
Using Scikit-Learn for Machine Learning in Python
Data scientists using Python must be comfortable and proficient in using scikit-learn, which is why Flatiron School’s Data Science Bootcamp emphasizes it throughout its curriculum.
Tech Trends
Rejecting the Null Hypothesis Using Confidence Intervals
After a discussion on the two primary methods of statistical inference, viz. hypothesis tests and confidence intervals, it is shown that these two methods are actually equivalent.
Artificial Intelligence
Hyperbolic Tangent Activation Function for Neural Networks
Activation functions play an important role in neural networks and deep learning algorithms. A common activation function is the hyperbolic tangent function, which is like the trigonometric tangent function, but defined using a hyperbola rather than a circle.
Artificial Intelligence
Why Do We Need Statistics for Data Science?
Data science needs statistics not only for descriptive and inferential statistics, but also for the statistical learning techniques of artificial intelligence.
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