No-nonsense data science and machine learning guides, mini-courses, and tutorials for busy people learning programming online. You can also download code cheat sheets, checklists, and worksheets to shorten the data science learning curve.
A free guide that breaks down exactly what computer science topic to learn and in what order. There are nine subjects (e.g., programming, math for CS, databases) and a suggested textbook or video lecture series for each.
foundations of programming databases torrent download
Course ObjectiveThe objective of this course is to introduce you to the fundamentals of databases by reviewing relational database models and designs, the foundations of SQL (structured query language), database modifications, and business intelligence applications.
Common Lisp Recipes is a collection of solutions to problemsand answers to questions you are likely to encounter when writingreal-world applications in Common Lisp. Written by an author whohas used Common Lisp in many successful commercial projects overmore than a decade, this book covers areas as diverse as webprogramming, databases, graphical user interfaces, integrationwith other programming languages, multi-threading, and mobiledevices as well as debugging techniques and optimization, to namejust a few. It is also the first Common Lisp book to tackle suchadvanced topics as environment access, logical pathnames, Graystreams, delivery of executables, pretty printing, setfexpansions, or changing the syntax of Common Lisp.
Catalog Description: Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications. Units: 4
Catalog Description: This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming language. Units: 4
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