Here are some incredible platforms where you can download datasets for your project:
Our World in Data https://ourworldindata.org/
World Health Organization (https://www.who.int/data/gho
Statcounter (https://gs.statcounter.com/
Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en
World Bank (https://data.worldbank.org/)
Our World in Data https://ourworldindata.org/
World Health Organization (https://www.who.int/data/gho
Statcounter (https://gs.statcounter.com/
Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en
World Bank (https://data.worldbank.org/)
โค7๐1
8 AI Tools Just for Fun:
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
Tattoosai
AI-powered Tattoo Generator: Your Personal Tattoo Artist
If you have an idea for a tattoo but can't find the right design, let our AI generate one within seconds. It lets you create the perfect design based on what you like, and it will give you unlimited options so that there's something for everyone.
๐9โค1๐1
1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Pythonโs lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Whatโs the F1 score? How would you use it?
The F1 score is a measure of a modelโs performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the โbucket of modelsโ method) and demonstrate how they could increase predictive power.
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Pythonโs lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Whatโs the F1 score? How would you use it?
The F1 score is a measure of a modelโs performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the โbucket of modelsโ method) and demonstrate how they could increase predictive power.
๐10
Python Notes ๐
https://t.iss.one/pythondevelopersindia/576
https://t.iss.one/pythondevelopersindia/576
๐4
๐ฅ Free Courses on Large Language Models
โชChatGPT Prompt Engineering for Developers
โชLangChain for LLM Application Development
โชBuilding Systems with the ChatGPT API
โชGoogle Cloud Generative AI Learning Path
โชIntroduction to Large Language Models with Google Cloud
โชLLM University
โชFull Stack LLM Bootcamp
โชChatGPT Prompt Engineering for Developers
โชLangChain for LLM Application Development
โชBuilding Systems with the ChatGPT API
โชGoogle Cloud Generative AI Learning Path
โชIntroduction to Large Language Models with Google Cloud
โชLLM University
โชFull Stack LLM Bootcamp
๐10โค1
Stack Overflow jumps into the generative AI world with OverflowAI
๐12
1. What are the different subsets of SQL?
Data Definition Language (DDL) โ It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) โ 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) โ It allows you to control access to the database. Example โ 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 โ 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 โ This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many โ This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships โ 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
Data Definition Language (DDL) โ It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) โ 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) โ It allows you to control access to the database. Example โ 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 โ 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 โ This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many โ This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships โ 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
โค8๐7
"๐ Data Analysis Tip: Have you ever wondered how outliers can impact your analysis? Outliers are data points that significantly differ from the rest of your dataset. They can skew results and affect the accuracy of your insights.
Tip: Before removing outliers, it's essential to understand their origin. Are they errors, natural variations, or something else? Removing or adjusting them without proper justification can lead to biased results.
Tip: Before removing outliers, it's essential to understand their origin. Are they errors, natural variations, or something else? Removing or adjusting them without proper justification can lead to biased results.
๐11
๐ 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
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
โค11๐7
Bu๐ถ๐น๐ฑ ๐ฅ๐ฒ๐๐๐บ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐s
1. Interviewai.me โข Mock interview with Al
2. Jobwizard.earlybird. rocks โข Auto fill job applicaions
3. Interviewgpt.a โข Interview questions
4. Majorgen.com โข Resume and cover letter builder
5. Metaview.ai โข Interview notes
6. Kadoa.com/joblens โข Personalized job recommendations
7. Huru.ai โข Mock interview and get feedback
8. Accio.springworks.in โข Resume scan
9. Interviewsby.a โข ChatGPT-based interview coach
10. MatchThatRoleAl.com โข Job search
11. Applyish.com โข Apply automatically
12. HnResumeToJobs.com โข Resume to jobs
13. FixMyResume.xyz โข Fix your resume
14. Resumatic.ai โข Create your resume with ChatGPT
15. Rankode.ai โข Rank your programming skills
Bonus: Apply for AI jobs โ https://t.iss.one/aijobz
1. Interviewai.me โข Mock interview with Al
2. Jobwizard.earlybird. rocks โข Auto fill job applicaions
3. Interviewgpt.a โข Interview questions
4. Majorgen.com โข Resume and cover letter builder
5. Metaview.ai โข Interview notes
6. Kadoa.com/joblens โข Personalized job recommendations
7. Huru.ai โข Mock interview and get feedback
8. Accio.springworks.in โข Resume scan
9. Interviewsby.a โข ChatGPT-based interview coach
10. MatchThatRoleAl.com โข Job search
11. Applyish.com โข Apply automatically
12. HnResumeToJobs.com โข Resume to jobs
13. FixMyResume.xyz โข Fix your resume
14. Resumatic.ai โข Create your resume with ChatGPT
15. Rankode.ai โข Rank your programming skills
Bonus: Apply for AI jobs โ https://t.iss.one/aijobz
๐19โค2
๐ 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
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
โค2๐1๐ฅ1
Microsoft is integrating python with MS Excel on cloud. So in newer updates you don't have to install anything extra and you'll able to leverage python libraries right within from excel
โค7
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 โpylab = 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 Pythonโs 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.
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 โpylab = 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 Pythonโs 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.
๐8โค5
Top 10 Computer Vision Project Ideas
1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
โค9๐1
๐คฉ Quick Roadmaps to Learn ๐คฉ
โค๏ธ Javascript
https://roadmap.sh/javascript
โค๏ธ Data Science
https://miro.medium.com/max/828/1*UQ9M5X6R1LVPzwc4bfnt9w.webp
โค๏ธ Frontend development
https://i0.wp.com/css-tricks.com/wp-content/uploads/2018/07/modern-front-end-developer.png?ssl=1
โค๏ธ Data Analyst Roadmap
https://t.iss.one/sqlspecialist/379
โค๏ธ AI/ML
https://i.am.ai/roadmap
โค๏ธ Javascript
https://roadmap.sh/javascript
โค๏ธ Data Science
https://miro.medium.com/max/828/1*UQ9M5X6R1LVPzwc4bfnt9w.webp
โค๏ธ Frontend development
https://i0.wp.com/css-tricks.com/wp-content/uploads/2018/07/modern-front-end-developer.png?ssl=1
โค๏ธ Data Analyst Roadmap
https://t.iss.one/sqlspecialist/379
โค๏ธ AI/ML
https://i.am.ai/roadmap
๐5โค3๐ฅฐ2
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
๐13โค2