Machine Learning Program with Andrew Ng (Stanford University)

ML colleagues, join over 3.5m students enrolled in this foundational program on machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). Also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio and database mining, Training modules include: 1) Linear Regression (single variable) and Algebra, 2) Linear Regression (multivariate) and Octave/Matlab Tutorial, 3) Logistic Regression and Regularization, 4) Neural Networks: Representation, 5) Neural Networks: Learning, 6) Advice for Applying Machine Learning and Machine Learning System Design, 7) Support Vector Machines, 8) Unsupervised Learning and Dimensionality Reduction, 9) Anomaly Detection and Recommender Systems, 10) Large Scale Machine Learning and 11) Application Example: Photo OCR.

Enroll today at: https://tinyurl.com/yxagguo8 

Much career success, Lawrence E. Wilson  - Artificial Intelligence Academy (AIA)