Important Machine Learning algorithms and their Hyperparameters

#machinelearning #datascience #statistics #algorithms

✴️ @AI_Python_EN

Why statistics should make you suspicious
Spiegelhalter on algorithm, luck, bias, probabilities, machine learning and AI.

https://lnkd.in/e-X9hXJ

#artificialintelligence #bias #ai #statistics #ai #bigdata

✴️ @AI_Python_EN

Here are some #statistics and research #journals I can recommend:

- Statistical Analysis and Data Mining (ASA)
- Analytics Journal (DMA)
- The American Statistician (ASA)
- Journal of the American Statistical Association (ASA)
- Statistics in Biopharmaceutical Research (ASA)
- Journal of Agricultural, Biological, and Environmental Statistics (ASA)
- Journal of Statistics Education (ASA)
- Statistics and Public Policy (ASA)
- Journal of Survey Statistics and Methodology (AAPOR and ASA)
- Journal of Educational and Behavioral Statistics (ASA)
- British Journal of Mathematical and Statistical Psychology (Wiley)
- Statistics Surveys (IMS)
- Stata Journal (StataCorp)
- The R Journal (R Project)
- Structural Equation Modeling: A Multidisciplinary Journal (Routledge)
- Journal of Business & Economic Statistics (ASA)
- Journal of Marketing Research (AMA)
- Journal of Computational and Graphical Statistics (ASA)
- Journal of Artificial General Intelligence (AGIS)

These are not purely theoretical publications and provide plenty of examples I can adapt for my own work. I try to read them as regularly as I can.

There's so much innovation happening in analytics that it's hard to keep up!

✴️ @AI_Python_EN

Don't stop sharing, done is better than perfect

For people who actively continue to blame, condemn and complain online, especially when reacting to content containing statistics, programming and machine learning that has been simplified, look for value in the imperfections of others.

We both know that machine learning models will never be perfect, as George P.Box said, "there are no perfect models, but some are useful". As with the content mentioned above, there are often reduced details to facilitate understanding, actionability, business value and expand the spread of knowledge.

Not all of us will face cases that are on each topic of the content mentioned above, but if we know in part, we can get the opportunity to work on a better process, even helping people.

Don't stop sharing, done is better than perfect

#programming #statistics #machinelearning

✴️ @AI_Python_EN

Machine Learning (ML) & Artificial Intelligence (AI): From Black Box to White Box Models in 4 Steps - Resources for Explainable AI & ML Model Interpretability.

✔️STEP 1 - ARTICLES

- (short) KDnuggets article: https://lnkd.in/eRyTXcQ

- (long) O'Reilly article: https://lnkd.in/ehMHYsr

✔️STEP 2 - BOOKS

- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y

- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N

✔️STEP 3 - COLLABORATE

- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ

✔️STEP 4 - PRACTICE

- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7

- Python Jupyter Notebooks: https://lnkd.in/dETegUH

#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare

✴️ @AI_Python_EN

#Statistics has many uses but, fundamentally, it's a systematic way of dealing uncertainty. When something is certain, there is no need to bring in a statistician or ask anyone for their council.

Since we're concerned with uncertainty, statisticians approach questions probabilistically. To conclude that something is likely to be true does not mean we're claiming it IS true, only that it's more likely to be true than not.

We may estimate this probability as being very high but, again, this is not saying the #probability is perfect (1.0).

Statisticians also think in terms of conditional probabilities, which means we've estimated the probability after having taken other information into account.

For instance, we might estimate the probability of a person buying a certain type of product within the next three months as 0.7 because he is a 25 year-old male. This estimate may have been made with a statistical model and data from thousands or millions of other consumers. For a 55 year-old woman our estimate might be 0.15.

Part of the challenge of being a statistician is that decision-makers often come to us for definitive yes-or-no answers. They can become irritated when we ask for more information or give them very qualified recommendations.

It ain't just math and programming!

tips: If someone says, for example, that A is not the only possible explanation for something and that B, C, or D are other possibilities, a common reaction is for the other party to conclude the first person is saying A is NOT a possible explanation. Humans are funny people.

✴️ @AI_Python_EN

As the author states: "work in process and even in an early dirty phase"
But still very cool 🙂

Book: Predictive Models: Visual Exploration, Explanation and Debugging With examples in R and Python By Przemyslaw Biecek


#book #datascience #machinelearning #statistics #programming_language

🌎 Book

✴️ @AI_Python_EN

share knowledge on one of basic topic in Statistics and Machine Learning.

"Assumptions of Linear Regression"

Understanding the assumptions is very important for anybody to build a robust model and improve the performance.

#machinelearning #AIML #statistics #artificialintelligence

https://lnkd.in/eJupcDZ

✴️ @AI_Python_EN