50 کامند مهم لینوکس
1. ls - view contents of directory (list)
2. pwd - path of the current directory
3. cd - change directoryn
4. mkdir - make new directory
5. mv - move files / rename files
6. cp - copy files
7. rm - remove files
8. touch - create blank new file
9. rmdir - delete directory
10. cat - list content of file to terminal
11. clear - clear terminal window
12. echo - move data into a file
13. less - Read text file one screen at a time
14. man - show manual of Linux commands
15. sudo - enables you to perform tasks that require administrative or root permissions
16. top - task manager in terminal
17. tar - used to archive multiple files into a tarball
18. grep - used to searching words in specific files
19. head - view first lines of any text file
20. tail - view last lines of any text file
21. diff - compares the contents of two files line by line
22. kill - used for killing unresponsive program
23. jobs - display all current jobs along with their statuses
24. sort - is a command line utility for sorting lines of text files
25. df - info about system disk
26. du - check how much space a file or directory takes
27. zip - to compress your files into a zip archive
28. unzip - to extract the zipped files from a zip archive
29. ssh - a secure encrypted connection between two hosts over and insecure network
30. cal - shows calendar
31. apt - command line tool for interaction with packaging system
32. alias - custom shortcuts used to represent a command
33. w - current user info
34. whereis - used to locate the binary, source, manual page files
35. whatis - used to get one-line man page description
36. useradd - used to create a new user
37. passwd - used to changing password of current user
38. whoami - print current user
39. uptime - print current time when machine starts
40. free - print free disk space info
41. history - print used commands history
42. uname - print detailed information about your Linux system
43. ping - to check connectivity status to a server
44. chmod - to change permissions of files and directories
45. chown - to change ownership of files and directories
46. find - using find searches for files and directories
47. locate - used to locate a file, just like the search command in Windows
48. ifconfig - print ip address stuff
49. ip a - similar to ifconfig but shortest print
50. finger - gives you a short dump of info about a user
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1. ls - view contents of directory (list)
2. pwd - path of the current directory
3. cd - change directoryn
4. mkdir - make new directory
5. mv - move files / rename files
6. cp - copy files
7. rm - remove files
8. touch - create blank new file
9. rmdir - delete directory
10. cat - list content of file to terminal
11. clear - clear terminal window
12. echo - move data into a file
13. less - Read text file one screen at a time
14. man - show manual of Linux commands
15. sudo - enables you to perform tasks that require administrative or root permissions
16. top - task manager in terminal
17. tar - used to archive multiple files into a tarball
18. grep - used to searching words in specific files
19. head - view first lines of any text file
20. tail - view last lines of any text file
21. diff - compares the contents of two files line by line
22. kill - used for killing unresponsive program
23. jobs - display all current jobs along with their statuses
24. sort - is a command line utility for sorting lines of text files
25. df - info about system disk
26. du - check how much space a file or directory takes
27. zip - to compress your files into a zip archive
28. unzip - to extract the zipped files from a zip archive
29. ssh - a secure encrypted connection between two hosts over and insecure network
30. cal - shows calendar
31. apt - command line tool for interaction with packaging system
32. alias - custom shortcuts used to represent a command
33. w - current user info
34. whereis - used to locate the binary, source, manual page files
35. whatis - used to get one-line man page description
36. useradd - used to create a new user
37. passwd - used to changing password of current user
38. whoami - print current user
39. uptime - print current time when machine starts
40. free - print free disk space info
41. history - print used commands history
42. uname - print detailed information about your Linux system
43. ping - to check connectivity status to a server
44. chmod - to change permissions of files and directories
45. chown - to change ownership of files and directories
46. find - using find searches for files and directories
47. locate - used to locate a file, just like the search command in Windows
48. ifconfig - print ip address stuff
49. ip a - similar to ifconfig but shortest print
50. finger - gives you a short dump of info about a user
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Stack and Concatenate Numpy Arrays in Python
https://www.pythonforbeginners.com/basics/stack-and-concatenate-numpy-arrays-in-python
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https://www.pythonforbeginners.com/basics/stack-and-concatenate-numpy-arrays-in-python
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Forwarded from Python4Finance
آشنایی با توزیع نرمال @Python4finance.pdf
615.1 KB
آشنایی با توزیع نرمال، و روش های نرمال سازی و استانداردسازی
در این اسلاید که مربوط به دوره آمار و احتمال علم داده است، مفهوم توزیع نرمال بررسی شده و با استفاده از پایتون، آزمون های نرمالیتی و روش های استانداردسازی و نرمال سازی بررسی می شود. مطالعه این اسلاید، به علاقه مندان آمار و نیز علاقه مندان یادگیری ماشین توصیه می شود.
#اسلاید
#آموزش
#توزیع_نرمال
#استاندارد_سازی
#نرمال_سازی
✅ پایتون برای مالی در تلگرام
https://t.me/python4finance
✅ پایتون برای مالی در بله
https://ble.ir/python4finance
در این اسلاید که مربوط به دوره آمار و احتمال علم داده است، مفهوم توزیع نرمال بررسی شده و با استفاده از پایتون، آزمون های نرمالیتی و روش های استانداردسازی و نرمال سازی بررسی می شود. مطالعه این اسلاید، به علاقه مندان آمار و نیز علاقه مندان یادگیری ماشین توصیه می شود.
#اسلاید
#آموزش
#توزیع_نرمال
#استاندارد_سازی
#نرمال_سازی
✅ پایتون برای مالی در تلگرام
https://t.me/python4finance
✅ پایتون برای مالی در بله
https://ble.ir/python4finance
Algorithm
Linear regression
Description
Finds a way to correlate each feature to the output to help predict future values.
Type
Regression
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Linear regression
Description
Finds a way to correlate each feature to the output to help predict future values.
Type
Regression
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Algorithm
Logistic regression
Description
Extension of linear regression that’s used for classification tasks. The output variable 3is binary (e.g., only black or white) rather than continuous (e.g., an infinite list of potential colors)
Type
Classification
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Logistic regression
Description
Extension of linear regression that’s used for classification tasks. The output variable 3is binary (e.g., only black or white) rather than continuous (e.g., an infinite list of potential colors)
Type
Classification
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Algorithm
Decision tree
Description
Highly interpretable classification or regression model that splits data-feature values into branches at decision nodes (e.g., if a feature is a color, each possible color becomes a new branch) until a final decision output is made
Type
Regression
Classification
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Decision tree
Description
Highly interpretable classification or regression model that splits data-feature values into branches at decision nodes (e.g., if a feature is a color, each possible color becomes a new branch) until a final decision output is made
Type
Regression
Classification
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Algorithm
Naive Bayes
Description
The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event.
Type
Regression
Classification
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Naive Bayes
Description
The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event.
Type
Regression
Classification
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Algorithm
Support vector machine
Description
Support Vector Machine, or SVM, is typically used for the classification task.
SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver.
Type
Regression (not very common)
Classification
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Support vector machine
Description
Support Vector Machine, or SVM, is typically used for the classification task.
SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver.
Type
Regression (not very common)
Classification
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Algorithm
Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
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Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
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Algorithm
AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
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AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
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Algorithm
Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
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Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
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Unsupervised learning
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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Algorithm Name
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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