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A Plethora of Machine Learning Articles: Part 1

Source: see here

In Part 1 of this short series, I have included the most interesting articles that I wrote in the last few years. This part focuses on the business analytics / BI / operational research aspects. The next parts will focus on

  • Mathematics, simulations, benchmarking algorithms based on synthetic data (in short, experimental data science)
  • Opinions, for instance about the value of a PhD in our field, or the use of some techniques
  • Methods, principles, rules of thumb, recipes, tricks

My articles are always written in simple English and accessible to professionals with typically one year of calculus or statistical training, at the undergraduate level. They are geared towards people who use data but are interesting in gaining more practical analytical experience. Managers and decision makers are part of my intended audience. The style is compact, geared towards people who do not have a lot of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path results as well as machine learning trade secrets and research material are frequently shared. References to more advanced literature (from myself and other authors) is provided for those who want to dig deeper in the interested topics discussed. 

Before starting, let me mention in section 1 two books that I wrote recently, available to all Data Science Central members.

1. Free books

  • Statistics: New Foundations, Toolbox, and Machine Learning Recipes

    Available here. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach.

    The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.

  • Applied Stochastic Processes

    Available here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject.

    It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.

2. Business related articles

These articles focus on business applications and other matters relevant to being a data scientist working in the Industry. They are accessible to a wide audience, in the sense that they are less technical than many of my 200+ other articles.

  1. New Stock Trading and Lottery Game Rooted in Deep Math
  2. Time series, Growth Modeling and Data Science Wizardy 
  3. How to Stabilize Data Systems, to Avoid Decay in Model Performance
  4. 22 Differences Between Junior and Senior Data Scientists
  5. The First Things you Should Learn as a Data Scientist - Not what yo...
  6. Difference between Machine Learning, Data Science, AI, Deep Learnin...
  7. 21 data science systems used by Amazon to operate its business
  8. Life Cycle of Data Science Projects
  9. 40 Techniques Used by Data Scientists
  10. Designing better algorithms: 5 case studies
  11. Architecture of Data Science Projects
  12. 24 Uses of Statistical Modeling (Part II)  | (Part I)
  13. The ABCD's of Business Optimization
  14. What you won't learn in stats classes
  15. Biased vs Unbiased: Debunking Statistical Myths

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent's articles and books, here.

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