Predictive Analytics
by Eric Siegel · Eric Siegel
Nontechnical account by former Columbia professor Eric Siegel of how organizations train models on historical data to predict individual behavior, such as who will buy, click, or default. Case studies explain ensemble models and uplift modeling, and where these predictions succeed or fail.
This link may earn us a small commission at no extra cost to you. Affiliate disclosure
More resources on Predictive Analytics
Distill.pub
Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes.
3Blue1Brown - Neural Networks series
Grant Sanderson's 3Blue1Brown channel offers visually intuitive and mathematically rigorous explanations of complex topics. His series on neural networks is particularly praised for making the core concepts, like backpropagation, understandable through animated visuals.
StatQuest: Linear Regression Clearly Explained
Josh Starmer walks through fitting a line by least squares, then explains R-squared and the p-value for the fit using simple visuals. Good for building intuition before meeting the algebra of regression.
Machine Learning
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Predictive Analytics Fundamentals
Illinois Tech course on Coursera introducing classification and clustering in R: k-nearest neighbors, naive Bayes, C5.0 decision trees, neural networks, support vector machines, k-means, and Apriori association rules. Learners finish able to train, compare, and interpret these models on tabular data.
Applied Predictive Modeling
Practitioner's text covering the full predictive modeling workflow in R: data preprocessing, resampling and overfitting control, linear, nonlinear, and tree-based regression and classification models, class imbalance, and feature selection. Worked examples use the authors' caret package; assumes basic statistics and some R.