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Here's a list of almost ~all regression models used in machine learning. Make sure to try them out.
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#MachineLearning #regression #codanics #ArtificialIntelligence #DataScience #analysis #DataAnalytics #model #algorithm #pakdatasci
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#MachineLearning #regression #codanics #ArtificialIntelligence #DataScience #analysis #DataAnalytics #model #algorithm #pakdatasci
Part 2: Biopython
Key features and examples.
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Key features and examples.
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Sequences.
A core concept in Biopython is the biological sequence, and this is represented by the Seq class. A Biopython Seq object is similar to a Python string in many respects: it supports the Python slice notation, can be concatenated with other sequences and is immutable.
A core concept in Biopython is the biological sequence, and this is represented by the Seq class. A Biopython Seq object is similar to a Python string in many respects: it supports the Python slice notation, can be concatenated with other sequences and is immutable.
Sequence annotation.
The SeqRecord class describes sequences along with information such as name description and features in the form of SeqFeature objects. Each SeqFeature object specifies the type of the feature and its location.
The SeqRecord class describes sequences along with information such as name description and features in the form of SeqFeature objects. Each SeqFeature object specifies the type of the feature and its location.
Biopython: an open-source collection of non-commercial Python tools for computational biology and bioinformatics It contains classes to represent biological sequences and sequence annotations
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- History:
Began in 1999 and it was first released in July 2000. It was developed during a similar time frame and with analogous goals to other projects that added bioinformatics capabilities to their respective programming languages, including BioPerl, BioRuby and BioJava.
Began in 1999 and it was first released in July 2000. It was developed during a similar time frame and with analogous goals to other projects that added bioinformatics capabilities to their respective programming languages, including BioPerl, BioRuby and BioJava.
- Design:
Follows the conventions used by the Python programming language to make it easier for users familiar with Python. For example, Seq and SeqRecord objects can be manipulated via slicing, in a manner similar to Python's strings and lists. #DataVisualization
Follows the conventions used by the Python programming language to make it easier for users familiar with Python. For example, Seq and SeqRecord objects can be manipulated via slicing, in a manner similar to Python's strings and lists. #DataVisualization
Strategies for Finding Data:
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#pythonkachilla2 #pythonkachilla #pakdatasci #DataScience #MachineLearning #DataVisualization
Browsing Data Compendia:
This is a good strategy if you are not sure what types of variables exist or what data would be relevant for your project
- Select a data compendia
- Determine the subject area or data type that your topic or variable falls under
- Read the descriptions
This is a good strategy if you are not sure what types of variables exist or what data would be relevant for your project
- Select a data compendia
- Determine the subject area or data type that your topic or variable falls under
- Read the descriptions
Searching by Topic:
This guide provides several links to data sources by topic. These links are by no mean exhaustive, but can be a good place to start and can help you get a sense of who are some of the major collectors of data in your topic area.
This guide provides several links to data sources by topic. These links are by no mean exhaustive, but can be a good place to start and can help you get a sense of who are some of the major collectors of data in your topic area.
A beginner's guide to interpreting Box plot.
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Box plot is also called box and whiskers plot.
It gives us information about different things which help us get insights of the data, these include median (Q2), first quartile (Q1), third quartile (Q3), minimum value, maximum value, outliers, and interquartile range (IQR).
It gives us information about different things which help us get insights of the data, these include median (Q2), first quartile (Q1), third quartile (Q3), minimum value, maximum value, outliers, and interquartile range (IQR).
1. The line in the middle of the box is the median value.
50% of the data is on one side of the median, and 50% of the data is on the other side of the median.
2. The first quartile (Q1) is the middle value between the median and minimum value in the data set. (See figure)
50% of the data is on one side of the median, and 50% of the data is on the other side of the median.
2. The first quartile (Q1) is the middle value between the median and minimum value in the data set. (See figure)
Shortcut keys for Microsoft Excel.
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1: Alt+Q, then enter the search term.
Move to the Tell me or Search field on the ribbon and type a search term for assistance or Help content.
Move to the Tell me or Search field on the ribbon and type a search term for assistance or Help content.
2: Alt+F
Open the File menu.
Open the File menu.
STATISTICS ๐is the most important subject to know if you are jumping into data science.
I have put together top 3 youtube playlists to get yourself started with statistics.
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I have put together top 3 youtube playlists to get yourself started with statistics.
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5 ๐๐๐ ๐๐ฎ๐ฉ๐๐ค๐ฃ ๐๐๐๐ง๐๐ง๐๐๐จ that you should master to become a ๐ฟ๐๐ฉ๐ ๐๐๐๐๐ฃ๐ฉ๐๐จ๐ฉ
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๐๐ช๐ข๐ฅ๐ฎ (๐๐ช๐ข๐๐ง๐๐๐๐ก ๐พ๐ค๐ข๐ฅ๐ช๐ฉ๐๐ฉ๐๐ค๐ฃ)
Numpy is used for numerical computations including matrix calculations and multidimensional array operations. Performing numerical computation in Python is time-consuming so we use NumPy for fast computation.
Numpy is used for numerical computations including matrix calculations and multidimensional array operations. Performing numerical computation in Python is time-consuming so we use NumPy for fast computation.
๐๐๐ฃ๐๐๐จ (๐ฟ๐๐ฉ๐ ๐ผ๐ฃ๐๐ก๐ฎ๐จ๐๐จ)
In Data Science, when you get the data you have to preprocess it to make it suitable for analysis. Pandas library provides us the tools to manipulate the data and make sense of the data. It is used for data cleaning/wrangling
In Data Science, when you get the data you have to preprocess it to make it suitable for analysis. Pandas library provides us the tools to manipulate the data and make sense of the data. It is used for data cleaning/wrangling
