Video Description
With the commercial successes of machine learning and cloud computing, many business people need just enough math to take advantage of open source frameworks for big data. This video course from Paco Nathan and Allen Day presents useful areas of advanced math in easy-to-digest morsels. If you’re familiar with high school Algebra 2 and basic statistics, you’re good to go.
You’ll learn newly introduced math concepts through business use cases, brief Python code examples, and lots of figures and illustrations. By the end of the course, you’ll understand how to leverage complex graphs, sparse matrices, Bayesian priors, optimization solvers, and other tools.
- Learn advanced math through simple equations and illustrations
- Get tangible examples such as Lego blocks for data workflows
- Explore the math examples through typical business use cases
- Understand how these concepts tie into common business frameworks
- Follow a case study of the Foobartendr.io company throughout the course
Table of Contents
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Introduction
- Introduction: Computational Thinking 00:46:16
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Abstract Algebra
- Abstract Algebra: Show Me the Monoid 00:15:00
- Abstract Algebra: Data Workflows 00:19:01
- Abstract Algebra: Functional Programming 00:23:44
- Abstract Algebra: Performance Bottlenecks 00:11:36
- Abstract Algebra: Monoids in Python 00:09:54
- Abstract Algebra: Open Source Frameworks 00:08:50
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Linear Algebra
- Linear Algebra: The Red Pill 00:12:24
- Linear Algebra: Linear Systems 00:07:51
- Linear Algebra: Least Squares Approximation 00:13:01
- Linear Algebra: Eigenvalues and Eigenvectors 00:07:53
- Linear Algebra: Principal Component Analysis 00:10:56
- Linear Algebra: Open Source Frameworks 00:05:27
- Linear Algebra: Algebraic Graph Theory 00:16:47
- Bayesian Statistics
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Optimization
- Optimization: Caterpillar Won't Be Building a Social Network 00:39:50
- Optimization: Gradient Descent 00:16:46
- Optimization: Neural Networks 00:13:33
- Optimization: Evolutionary Algorithms 00:08:43
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Summary
- Summary 00:07:55