This document provides an overview of the book "Machine Learning for Hackers" and the MLDM Monday meetup. It summarizes the key points of each chapter, which cover basic R, supervised learning techniques like classification and regression, unsupervised learning techniques like PCA and clustering, and a concluding chapter on model comparison. Sample R codes from the book are available online. The meetup will introduce machine learning concepts and use two example datasets to practice basic data analysis and cleaning in R.
PyData SF 2016 --- Moving forward through the darknessChia-Chi Chang
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This document discusses various types of "blindness" that can occur when applying machine learning modeling procedures and techniques. It notes that modeling procedures often focus on decomposing problems and data in a way that can lose important connections or information. Specific issues highlighted include the gap between problems and available data, information loss when converting data to vectors, disconnects between mathematical concepts and real-world applications, limitations of individual ML techniques, and challenges with new data and labels. The document advocates thinking more from both data-driven and problem-driven perspectives, and considering alternative techniques that can bridge gaps, such as metric learning and one-versus-all classifiers.
Learning notes of r for python programmer (Temp1)Chia-Chi Chang
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R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.
how to learn quantmod and quantstrat by yourselfChia-Chi Chang
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This document provides instructions and resources for learning the R packages quantmod and quantstrat on your own. It lists the quantmod and quantstrat courses on DataCamp and links to the quantstrat documentation on GitHub to learn how to manipulate time series data and conduct financial trading simulations in R.
This document discusses communicating effectively with data by taking both a problem-driven and data-driven approach. It emphasizes understanding the problem behind the data as well as the information behind the problem to generate business insights. Both the problem and data should inform each other.
- The document provides an agenda for a presentation on mining trading strategies with R using quantstrat and R packages.
- It includes quick surveys of the audience, an overview of the architecture of a trading system, hands-on sessions on quantmod, PerformanceAnalytics, blotter and quantstrat, and discussions of basic concepts in quantitative trading and machine learning applications.
- The presenter is George Chang from Taiwan and organizes the Taiwan R User Group and MLDM Monday for applying machine learning in the real world through hands-on practice.
This document discusses communicating effectively with data by taking both a problem-driven and data-driven approach. It emphasizes understanding the problem behind the data as well as the information behind the problem to generate business insights. Both the problem and data should inform each other.
- The document provides an agenda for a presentation on mining trading strategies with R using quantstrat and R packages.
- It includes quick surveys of the audience, an overview of the architecture of a trading system, hands-on sessions on quantmod, PerformanceAnalytics, blotter and quantstrat, and discussions of basic concepts in quantitative trading and machine learning applications.
- The presenter is George Chang from Taiwan and organizes the Taiwan R User Group and MLDM Monday for applying machine learning in the real world through hands-on practice.
65. Key Features in quantmod
● Time Series Object: zoo, xts
● Data Importers: getSymbols
● Technical Analysis: TTR
● Draw Candlestick Lines: chartSeries