🔅 Voice Recorder in Python
pip install sounddevice
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pip install sounddevice
import sounddevice
from scipy.io.wavfile import write
#sample_rate
fs=44100
#Ask to enter the recording time
second = int(input("Enter the Recording Time in second: "))
print("Recording…\n")
record_voice = sounddevice.rec(int(second * fs),samplerate=fs,channels=2)
sounddevice.wait()
write("MyRecording.wav",fs,record_voice)
print("Recording is done Please check you folder to listen recording")Join us for more -
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Python Code to remove Image Background
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from rembg import remove
from PIL import Image
image_path = 'Image Name' ## ---> Change to Image name
output_image = 'ImageNew' ## ---> Change to new name your image
input = Image.open(image_path)
output = remove(input)
output.save(output_image)The most popular programming languages:
1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala
According to the Latest GitHub Repositories
1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala
According to the Latest GitHub Repositories
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🟢 7 valuable resources that I used to prepare for data science interviews!
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
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Here are a few project ideas that could help you stand out:
Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.
Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.
Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.
Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.
Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.
By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.
Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.
Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.
Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.
Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.
Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.
Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.
By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.
Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.
👍4❤1
Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
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