Machine & Deep Learning Compendium

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Probability & Statistics

β**Coursera course**** on probabilities - for data science, actually quite good in explaining a lot of the basic tools,prob, conditional, distributions, sampling, CI, hypothesis, etc.
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**I.e, Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.****The problems considered by probability and statistics are inverse to each other.****In probability theory we consider some underlying process which has some randomness or uncertainty modeled by random variables, and we figure out what happens.**

**In statistics we observe something that has happened, and try to figure out what underlying process would explain those observations.**

**Finally, probability theory is mainly concerned with the deductive part, statistics with the inductive part of modeling processes with uncertainty**

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**Std vs variance****- std is in the same metric as the mean, is the root of variance., allows outliers to influence, will not result in samples cancelling each other without the square root in the formula.**

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**Least squares regresssion****It works by making the total of the square of the errors as small as possible (that is why it is called "least squares"** - 9.
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**every individual we are interested in studying, and a sample, consisting of the individuals that are selected from the population.****in probability: would start with us knowing everything about the composition of a population, and then would ask, βWhat is the likelihood that a selection, or sample, from the population, has certain characteristics?β****In statistics: we have no knowledge about the types of socks in the drawer. we infer properties about the population on the basis of a random sample.**

**Finding out the probability of an event****Of two consecutive events (multiplication)****Of several events (sum)****Etc..**

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Last modified 5mo ago