A Plethora of Machine Learning Tricks, Recipes, and Statistical Models

Source: See article #5, partly 1

Part 2 of this fast sequence focused on fundamental strategies, see proper right here. In this Part 3, you may discover a quantity of machine learning strategies and recipes, many with a statistical style. These are articles that I wrote within the earlier couple of years. The full sequence will attribute articles related to the subsequent factors of machine learning:

• Mathematics, simulations, benchmarking algorithms based totally on synthetic info (briefly, experimental info science)
• Opinions, as an illustration regarding the price of a PhD in our space, or the use of some strategies
• Methods, concepts, tips of thumb, recipes, strategies
• Business analytics 
• Core Techniques 

My articles are always written in simple English and accessible to professionals with normally one yr of calculus or statistical teaching, on the undergraduate stage. They are geared within the route of people who use info nevertheless are attention-grabbing in gaining further smart analytical experience. Managers and willpower makers are half of my supposed viewers. The mannequin is compact, geared within the route of people who would not have somewhat lots of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path outcomes along with machine learning commerce secrets and techniques and methods and evaluation supplies are incessantly shared. References to further superior literature (from myself and completely different authors) is equipped for a lot of who have to dig deeper inside the topics talked about. 

1. Machine Learning Tricks, Recipes and Statistical Models

These articles take care of strategies which have enormous features or which may be in some other case fundamental or seminal in nature.

1. Defining and Measuring Chaos in Data Sets: Why and How, in Simple Words
2. Hurwitz-Riemann Zeta And Other Special Probability Distributions
3. Maximum runs in Bernoulli trials: simulations and outcomes
4. Moving Averages: Natural Weights, Iterated Convolutions, and Central Limit Theorem
5. Amazing Things You Did Not Know You Could Do in Excel
6. New Tests of Randomness and Independence for Sequences of Observations
7. Interesting Application of the Poisson-Binomial Distribution
8. Alternative to the Arithmetic, Geometric, and Harmonic Means
9. Bernouilli Lattice Models – Connection to Poisson Processes
10. Simulating Distributions with One-Line Formulas, even in Excel
11. Simplified Logistic Regression
12. Simple Trick to Normalize Correlations, R-squared, and so on
13. Simple Trick to Remove Serial Correlation in Regression Models
14. A Beautiful Result in Probability Theory
15. Long-range Correlations in Time Series: Modeling, Testing, Case Study
16. Difference Between Correlation and Regression in Statistics

2. Free books

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

  Available proper right here. In about 300 pages and 28 chapters it covers many new topics, offering a current perspective on the subject, along with tips of thumb and recipes which may be simple to automate or mix in black-box packages, along with new model-free, data-driven foundations to statistical science and predictive analytics. The technique focuses on sturdy strategies; it is bottom-up (from features to idea), in distinction to the usual top-down technique.

  The supplies is accessible to practitioners with a one-year college-level publicity to statistics and probability. The compact and tutorial mannequin, that features many features with fairly just a few illustrations, is aimed towards practitioners, researchers, and executives in assorted quantitative fields.

• Applied Stochastic Processes

  Available proper right here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This e-book is supposed for professionals in info science, laptop science, operations evaluation, statistics, machine learning, large info, and arithmetic. In 100 pages, it covers many new topics, offering a current perspective on the subject.

  It is accessible to practitioners with a two-year college-level publicity to statistics and probability. The compact and tutorial mannequin, that features many features (Blockchain, quantum algorithms, HPC, random amount know-how, cryptography, Fintech, web crawling, statistical testing) with fairly just a few illustrations, is aimed towards practitioners, researchers and executives in assorted quantitative fields.

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About the creator:  Vincent Granville is a data science pioneer, mathematician, e-book creator (Wiley), patent proprietor, former post-doc at Cambridge University, former VC-funded govt, with 20+ years of firm experience along with CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent can be self-publisher at DataShaping.com, and based mostly and co-founded a pair of start-ups, along with one with a worthwhile exit (Data Science Central acquired by Tech Target). You can entry Vincent’s articles and books, proper right here.