UC Berkeley researchers detect ‘silent speech’ with electrodes and AI

https://venturebeat.com/2020/11/24/uc-berkeley-researchers-detect-silent-speech-with-electrodes-and-ai/?fbclid=IwAR3kd6ZIsPw0gGjetTunLa_n52JG9UHHWFsz2bcneFOpowjagKNF4_RuuRE

Top 10 python Libraries for Data Science

Coursera Courses (Free)

100 online courses from Coursera are $0 now through December 31

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@pythonworld

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Shutdown PC using Python:

import os
os.system("shutdown /s / 1")

Data Science #Interview Preparation:

30 days of Data Science InterviewPreparation - All in one

Data Science #Interview Preparation:

Day 1 Questions:

Q1. What is the difference between AI, Data Science, ML, and DL?
Q2. What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?
Q3. Describe the general architecture of Machine learning.
Q4. What is Linear Regression?
Q5. OLS Stats Model (Ordinary Least Square)
Q6. What is L1 Regularization (L1 = lasso) ?
Q7. L2 Regularization(L2 = Ridge Regression)
Q8. What is R square(where to use and where not)?
Q9. What is Mean Square Error?
Q10. Why Support Vector Regression? Difference between SVR and a simple regression model?

Python World

Fibonacci Numbers via Matrix Multiplication#

The formula explains why the ratio of consecutive Fibonacci numbers tends to the golden ratio.

Data Scientist Salary

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#Book
Open Data Structures: An Introduction
Pat Morin

#Book
Open Data Structures: An Introduction
Pat Morin

Data Science #Interview Preparation Day 3

Day 3 Questions:
Q1. How do you treat heteroscedasticity in regression?
Q2. What is multicollinearity, and how do you treat it?
Q3. What is market basket analysis? How would you do it in Python?
Q4. What is Association Analysis? Where is it used?
Q5. What is KNN Classifier ?
Q6. What is Pipeline in sklearn ?
Q7. What is Principal Component Analysis(PCA), and why we do?
Q8. What is t-SNE?
Q9. VIF(Variation Inflation Factor), Weight of Evidence & Information Value. Why and when to use?
Q10: How to evaluate that data does not have any outliers ?
Q11: What you do if there are outliers?
Q12: What are the encoding techniques you have applied with Examples ?
Q13: Tradeoff between bias and variances, the relationship between them.
Q14: What is the difference between Type 1 and Type 2 error and severity of the error?
Q15: What is binomial distribution and polynomial distribution?
Q16: What is the Mean Median Mode standard deviation for the sample and population?
. . .