Basic Stats

Probability: 

https://www.investopedia.com/terms/c/conditional_probability.asp

https://towardsdatascience.com/machine-learning-probability-statistics-f830f8c09326

https://www.probabilitycourse.com/chapter1/1_4_5_solved3.php


Statistics :

Follow jason Brownlee Book 

https://towardsdatascience.com/machine-learning-probability-statistics-f830f8c09326

Eigen Vectors And Eigen Values :


Probability Distributions - Discrete Probability Distribution - pmf :




Checking hwther the probability is PMF or not ?:




Probability density function and  Cummulative density function :



Normalization:

https://www.calculushowto.com/types-of-functions/normalized-function-normalized-data-and-normalization/

https://www.statisticshowto.com/probability-and-statistics/normal-distributions/

https://www.youtube.com/watch?v=2tuBREK_mgE&ab_channel=SimpleLearningPro

The terms normalization and standardization are sometimes used interchangeably, but they usually refer to different things. Normalization usually means to scale a variable to have values between 0 and 1, while standardization transforms data to have a mean of zero and a standard deviation of 1. This standardization is called a z-score, and data points can be standardized with the following formula:



Z-Score-:



Central Limit Theorem :


Random Variable and Sample Variable 

Population(meu) and Sample Mean(X bar):


LOg Normal Distribution:
Example are Income of the employee
Example are Comments size 


Inter Quartile Range:









Comments

Post a Comment

Popular posts from this blog

Presentation_Rashmi

Image
 SLIDE 1 What is SVM? “Support Vector Machine” (SVM) is a supervised  machine learning algorithm  which can be used for both classification or regression challenges. However,  it is mostly used in classification problems. SLIDE 2 how does it work ? Identify the right hyper-plane (Scenario-1): SLIDE3: Identify the right hyper-plane (Scenario-2):  SLIDE4: Here, maximizing the distances between nearest data point (either class) and hyper-plane will help us to decide the right hyper-plane. This distance is called as  Margin SLIDE5: Identify the right hyper-plane (Scenario-3): SLIDE6: Find the hyper-plane to segregate to classes  SLIDE  Cost function and Gradient Function In the SVM algorithm, we are looking to maximize the margin between the data points and the hyperplane. The loss function that helps maximize the margin is hinge loss. SLIDE  Regularization The objective of the regularization parameter is to balance the margin maximizat...
Read more

MySQL : Structured Query Language

Image
The acronym SQL stands for Structured Query Language SQL is the standard language for data manipulation SQL is the standard language for relational database management. Three Main Uses of SQL are : Read ,Write , Update SQL is used for many things; however, Data Retrieval  was stressed as THE MAIN THING used by Data Scientist Data Scientist might do a little of the analysis using SQL, however the main thing they're using SQL for is data retrieval. Learning Objective: Explain why thinking before coding is important  Explain why it is important to understand how data in a database is relate to each other  Describe what a database is  Think before you do:    What is the problem you are trying to do..?     What I mean by this is it's easy when you're doing work or  we're trying to produce a result or solve a problem to just go in and start doing.  By that I mean you go in and start to write code or maybe start to write some  type of emai...
Read more

spoken

  Machine learning  is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  basically Machine learning  focuses on the development of computer programs that can access data and use it learn for themselves. Currently,  machine learning  has been used in multiple fields and industries. For  example , medical diagnosis, image processing, prediction, classification,   regression etc Slide 2:  Machine Learning is categories in 3 parts  Supervised Learning  Unsupervised Learning Reinforecement Learning Slide 3: Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X) The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variable...
Read more