Ten Thousand 4
This topic matters as it relates to learning about statistics, probability and how to implement dunder methods in python.
Dunder Methods
Dunder methods (aka magic or special methods) are a set of predefined methods we can use to enrich our classes. They start and end with double underscores. (e.g.) __init__ or __str__.
They let us emulate the behavior of built-in types. For example, to get the length of a string we can call len('string'). But an empty class definition doesn’t support this behavior out of the box, so we can add a __len__ dunder method to the class. (e.g.)
class LenSupport:
def __len__(self):
return 42
obj = LenSupport()
len(obj)
# 42
This design is known as the Python data model and allows us to tap into rich language features like sequences, iteration, operator overloading, attribute access, etc.
A few examples:
__init__object constructor__repr__string representation of an object__str__string representation of an object nicely printable__len__returns length of an object__getitem__gets the value of an item__reversed__returns a sequence object in reverse order__add__adds attributes of an object
Statistics - Probability
Probability seeks to answer the question, “What is the chance of an event happening?” An event is some outcome of interest. To calculate the chance of an event happening, we also need to consider all the other events that can occur. For example a coin toss, only two events can happen, it can either be heads or tails.
- Sample space - The set of all possible events that can happen.
To calculate the probability of an event occurring, we count how many times are event of interest can occur and dividing it by the sample space. To avoid making assumptions we gather data. We use statistics to calculate probabilities based on observations from the real world and check how it compares to the ideal.
- Three Sigma Rule
The Three Sigma rule, aka as the empirical rule or 68-95-99.7 rule, is an expression of how many of our observations fall within a certain distance of the mean.
- Z-score
The Z-score is a simple calculation that answers the question, “Given a data point, how many standard deviations is it away from the mean?”
Intro to Statistics
Statistics is a collections of procedures and principles for gaining information in order to make decisions when faced with uncertainty.
Three major concepts
- Statistical Features
Probably the most used statistic concept in data science. Help us explore dataset to gain valuable insights. Like bias, variance and many others.
- Probability Distributions
Define the percent chance that some event will occur, and we can use them to understand the spread of data.
- Bayesian Statistics
Expresses probability as a degree of belief in an event which can change as new information is gathered rather than a fixed value based on frequency.
AI Guru makes $238,800 with misleading paid course. doesn’t credit developers
Seems like Siraj Raval is yet another public figure that has been corrupted by greed and the twisted obsession of social media status and popularity. It is very unfortunate because he is a talented educator, and could have used his platform in an honest and ethical way to impact a larger number of people in a positive manner.
Things I want to know more about
- I would like to know more about implementing dunder methods in python and understanding better probability and the three major concepts in statistics.