Hi, I’m Lawrence McDaniel and I’m a technology freelancer. The rapid evolution of IT infrastructure cloud services combined with the sudden and staggering accumulation of electronic data has catalyzed an explosion of applied science and innovation for many ideas that only a few years ago were still not much more than theory. The math hasn’t gotten any easier, and IT infrastructure has actually become a lot more complex, however, If you understand both of these disciplines and you also are a halfway decent software engineer then the sky is the limit in terms of what you can do.
Fortunately some of the greatest minds on earth, like Dr. Andrew Ng at Stanford University for example, have paved the way for engineers like me to implement incredible machine learning and artificial intelligence systems that leverage big data sets to attack problem spaces like computer vision, pattern recognition in highly dynamic environments (like debt, equities and commodities secondary markets for example), big data classification and insight problems, and robotics.
Regression | Multi-variable Linear, Polynomial and Logistic regression models leveraging a plethora of cost optimization strategies | |
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Neural Networks | For image processing, compression, robotics, OCR and other non-linear problem spaces. | |
Support Vector Machines | For classification problems in non-linear environments like text categorization, image sorting and hand-written character recognition | |
Anomaly Detection | Supervised learning algorithms to identify potential service failures, factory defects and other types of outliers in highly dynamic and non-linear environments. | |
Recommender Systems | Content-based recommendation engines e-commerce optimizations and other predictive customer recommendations | |
Unsupervised Learning | Clustering and K-means algorithms for applications like market segmentation, social network analysis and IT infrastructure management |