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This video follows from where we left off in Part 1 in this series on the details of Logistic Regression. This time we’re going to talk about how the squiggly line is optimized to best fit the data.

NOTE: In statistics, machine learning and most programming languages, the default base for the log() function is 'e'. In other words, when I write, "log()", I mean "natural log()", or "ln()". Thus, the log to the base 'e' of 2.717 = 1.

#statquest #logistic #MLE #machine-learning

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Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually defined and derived in a pattern-specific or a distribution-specific manner. For instance, Logistic Regression is a traditional machine learning algorithm meant specifically for a binary classification problem. Linear Regression is a traditional machine learning algorithm meant for the data that is linearly distributed in a multi-dimensional space. One specific algorithm cannot be applied for a problem of different nature.

To this end, Maximum Likelihood Estimation, simply known as MLE, is a traditional probabilistic approach that can be applied to data belonging to any distribution, i.e., Normal, Poisson, Bernoulli, etc. With prior assumption or knowledge about the data distribution, Maximum Likelihood Estimation helps find the most likely-to-occur distribution parameters. For instance, let us say we have data that is assumed to be normally distributed, but we do not know its mean and standard deviation parameters. Maximum Likelihood Estimation iteratively searches the most likely mean and standard deviation that could have generated the distribution. Moreover, Maximum Likelihood Estimation can be applied to both regression and classification problems.

Therefore, Maximum Likelihood Estimation is simply an optimization algorithm that searches for the most suitable parameters. Since we know the data distribution a priori, the algorithm attempts iteratively to find its pattern. The approach is much generalized, so that it is important to devise a user-defined Python function that solves the particular machine learning problem.

#developers corner #likelihood #log likelihood #maximum likelihood estimation #mle #probability distribution #python #regression #statistics

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This video follows from where we left off in Part 1 in this series on the details of Logistic Regression. This time we’re going to talk about how the squiggly line is optimized to best fit the data.

NOTE: In statistics, machine learning and most programming languages, the default base for the log() function is 'e'. In other words, when I write, "log()", I mean "natural log()", or "ln()". Thus, the log to the base 'e' of 2.717 = 1.

#statquest #logistic #MLE #machine-learning

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Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.

In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.

**Regression Analysis **is a statistical process for estimating the relationships between the **dependent variables ( say Y)** and one or more

#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning

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Posted by **Carlton Gibson** on Tháng 6 2, 2021

In accordance with our security release policy, the Django team is issuing Django 3.2.4, Django 3.1.12, and Django 2.2.24. These release addresses the security issue detailed below. We encourage all users of Django to upgrade as soon as possible.

Staff members could use the **admindocs** **TemplateDetailView** view to check the existence of arbitrary files. Additionally, if (and only if) the default admindocs templates have been customized by the developers to also expose the file contents, then not only the existence but also the file contents would have been exposed.

As a mitigation, path sanitation is now applied and only files within the template root directories can be loaded.

This issue has low severity, according to the Django security policy.

Thanks to Rasmus Lerchedahl Petersen and Rasmus Wriedt Larsen from the CodeQL Python team for the report.

**URLValidator**, **validate_ipv4_address()**, and **validate_ipv46_address()** didn’t prohibit leading zeros in octal literals. If you used such values you could suffer from indeterminate SSRF, RFI, and LFI attacks.

**validate_ipv4_address()** and **validate_ipv46_address()** validators were not affected on Python 3.9.5+.

This issue has medium severity, according to the Django security policy.

- Django main branch
- Django 3.2
- Django 3.1
- Django 2.2

#django #weblog #django security releases issued: 3.2.4, 3.1.12, and 2.2.24 #3.2.4 #3.1.12 #2.2.24

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Linear Regression and Logistic Regression are** two algorithms of machine learning **and these are mostly used in the data science field.

**Linear Regression**:> It is one of the algorithms of machine learning which is used as a technique to solve various use cases in the data science field. It is generally used in the case of **continuous output**. For e.g if ‘Area’ and ‘Bhk’ of the house is given as an input and we have found the ‘Price’ of the house, so this is called a regression problem.

Mechanism:> In the diagram below X is input and Y is output value.

#machine-learning #logistic-regression #artificial-intelligence #linear-regression