Machine learning tools allow computers to become more accurate in predicting outcomes. The computer’s software makes decisions based on experiences rather than programming. The algorithm (basically a series of instructions) collects data on its interactions, and that data is used as feedback for the algorithm, which changes its behavior and responses, improving them over time. […]
What Can Artificial Intelligence Do for Me? (Part 2)
In my previous blog post, I described some concrete techniques and surveyed some early approaches to artificial intelligence (AI) and found that they still offer attractive opportunities for improving the user experience. In this post, we’ll look at some more mathematical and algorithmic approaches to creating usable business intelligence from big piles of data. Regression Analysis Regression […]
Ensemble Models: Bagging and Boosting
Click to learn more about author Rosaria Silipo. Ensemble models combine multiple learning algorithms to improve the predictive performance of each algorithm alone. There are two main strategies to ensemble models — bagging and boosting — and many examples of predefined ensemble algorithms. Bootstrap aggregation, or bagging, is an ensemble meta-learning technique that trains many […]
Data Science in 90 Seconds: Natural Language Processing
Click to learn more about video blogger Laura Kahn. This is Lesson 18 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds: Deep Learning
Click to learn more about video blogger Laura Kahn. This is Lesson 16 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds: Choosing a Machine Learning Approach
Click to learn more about video blogger Laura Kahn. This is Lesson 17 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds: Artificial Neural Networks
Click to learn more about video blogger Laura Kahn. This is Lesson 15 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds: Deep Learning
Click to learn more about video blogger Laura Kahn. This is Lesson 14 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds – Lesson 13: LASSO
Click to learn more about video blogger Laura Kahn. This is Lesson 13 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]
Data Science in 90 Seconds: Ridge Regression
Click to learn more about video blogger Laura Kahn. This is Lesson 12 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, […]