Data Science & Machine Learning
26 Dec 2023 07:30
Machine Learning Interview Questions
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Data Science & Machine Learning
20 Dec 2023 17:41
Data Science & Machine Learning Project Discussions
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/channel/Kaggle_Group
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Data Science & Machine Learning
24 Aug 2023 05:28
800 Data Science Questions with Answers
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Data Science & Machine Learning
23 Aug 2023 19:29
Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
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Data Science & Machine Learning
23 Aug 2023 16:29
📚 9 must-have Python developer tools.
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
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Data Science & Machine Learning
16 Aug 2023 05:14
Bu𝗶𝗹𝗱 𝗥𝗲𝘀𝘂𝗺𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄s
1. Interviewai.me • Mock interview with Al
2. Jobwizard.earlybird. rocks • Auto fill job applicaions
3. Interviewgpt.a • Interview questions
4. Majorgen.com • Resume and cover letter builder
5. Metaview.ai • Interview notes
6. Kadoa.com/joblens • Personalized job recommendations
7. Huru.ai • Mock interview and get feedback
8. Accio.springworks.in • Resume scan
9. Interviewsby.a • ChatGPT-based interview coach
10. MatchThatRoleAl.com • Job search
11. Applyish.com • Apply automatically
12. HnResumeToJobs.com • Resume to jobs
13. FixMyResume.xyz • Fix your resume
14. Resumatic.ai • Create your resume with ChatGPT
15. Rankode.ai • Rank your programming skills
Bonus: Apply for AI jobs → https://ai-jobs.net/
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Data Science & Machine Learning
14 Aug 2023 17:06
Probability and Statistics
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Data Science & Machine Learning
13 Aug 2023 08:52
📚 9 must-have Python developer tools.
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
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Data Science & Machine Learning
04 Aug 2023 18:46
📚 Machine Learning Automation with TPOT
Dario Radečić, 2021
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Data Science & Machine Learning
04 Aug 2023 16:02
1. What are the different subsets of SQL?
Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
3. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
4. What is Normalization and what are the advantages of it?
Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
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Data Science & Machine Learning
30 Jul 2023 15:42
Stack Overflow jumps into the generative AI world with OverflowAI
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Data Science & Machine Learning
21 Jul 2023 17:47
Python Programming Notes 📝
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Data Science & Machine Learning
15 Jul 2023 08:03
Building IoT Visualizations using Grafana
Rodrigo Juan Hernandez, 2022
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