Stanford University offers a Machine Learning Course. The course is 11 weeks long, tuition is free (or $49 if you want a certificate upon completion of the course). The next session begins March 21, enrollment ends March 12. Here's the syllabus and enrollment link: https://www.coursera.org/learn/machine-learning/ Code: Syllabus Week 1 Introduction Linear Regression with One Variable Linear Algebra Review Welcome Introduction Review Other Materials Model and Cost Function Parameter Learning Review Linear Algebra Review Review Quiz: Introduction Quiz: Linear Regression with One Variable Week 2 Linear Regression with Multiple Variables Octave Tutorial Environment Setup Instructions Multivariate Linear Regression Computing Parameters Analytically Review Octave Tutorial Submitting Programming Assignments Review Quiz: Linear Regression with Multiple Variables Assignment: Linear Regression Quiz: Octave Tutorial Week 3 Logistic Regression Regularization Classification and Representation Logistic Regression Model Multiclass Classification Review Solving the Problem of Overfitting Review Quiz: Logistic Regression Assignment: Logistic Regression Quiz: Regularization Week 4 Neural Networks: Representation Motivations Neural Networks Applications Review Quiz: Neural Networks: Representation Assignment: Multi-class Classification and Neural Networks Week 5 Neural Networks: Learning Cost Function and Backpropagation Backpropagation in Practice Application of Neural Networks Review Quiz: Neural Networks: Learning Assignment: Neural Network Learning Week 6 Advice for Applying Machine Learning Machine Learning System Design Evaluating a Learning Algorithm Bias vs. Variance Review Building a Spam Classifier Handling Skewed Data Using Large Data Sets Review Quiz: Advice for Applying Machine Learning Assignment: Regularized Linear Regression and Bias/Variance Quiz: Machine Learning System Design Week 7 Support Vector Machines Large Margin Classification Kernels SVMs in Practice Review Quiz: Support Vector Machines Assignment: Support Vector Machines Week 8 Unsupervised Learning Dimensionality Reduction Clustering Review Motivation Principal Component Analysis Applying PCA Review Quiz: Unsupervised Learning Quiz: Principal Component Analysis Assignment: K-Means Clustering and PCA Week 9 Anomaly Detection Recommender Systems Density Estimation Building an Anomaly Detection System Multivariate Gaussian Distribution (Optional) Review Predicting Movie Ratings Collaborative Filtering Low Rank Matrix Factorization Review Quiz: Anomaly Detection Quiz: Recommender Systems Assignment: Anomaly Detection and Recommender Systems Week 10 Large Scale Machine Learning Gradient Descent with Large Datasets Advanced Topics Review Quiz: Large Scale Machine Learning Week 11 Application Example: Photo OCR Photo OCR Review Conclusion Quiz: Application: Photo OCR See also: https://www.coursera.org/about/partners http://www.blueowlpress.com/machine-learning-course-from-stanford-and-coursera "The ever-popular machine learning course sponsored by Coursera and Stanford University, taught by Andrew Ng, is beginning a new session. Over 10,000 people have already enrolled in this session."
Linear Algebra and Linear Regression... I don't recommend those classes. I had them in college and they caused the breakout for me with one of the hottest girls on campus.
Don't forget course at MIT's OpenCourseWare This one looks pretty good too. http://ocw.mit.edu/courses/electric...l-and-signal-processing-spring-2010/index.htm And here's a link to get lots of book for free: (ONLY for the SELFISH or POOR guys) http://gen.lib.rus.ec
Andrew Ng's course is awesome. I took it a few years ago. It makes you understand how gradient descent, recomender systems and neural nets work underneath the hood. It also gives a great foundation on machine learning, understanding the high variance/high bias trade off , the linear algebra involved etc. Berkley's artificial intelligence course is also great, and may be more easily used for trading since it is focused around reinforcement learning.