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Prof. Phd. Manoel Gadi
Spain
เข้าร่วมเมื่อ 30 ม.ค. 2014
Channel covers tech and academia including probability, statistics, web development, programming, blockchain, data analysis, and academic life. The channel offers tutorials, lectures, and discussions for those interested in tech and research.
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Concentrating for ADHD
Series of Music to Enhance Concentration for Studying and Working by Prof. Manoel Gadi. Subscribe for new songs every week.
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R Programming hands on with R code
มุมมอง 612 ชั่วโมงที่ผ่านมา
R Programming hands on with R code #Rprogramming #dataanalysis #machinelearning #dataviz
Concentrating to play (pop style)
มุมมอง 121 ชั่วโมงที่ผ่านมา
Series of Music to Enhance Concentration for Studying and Working by Prof. Manoel Gadi. Special for ADHD people. Subscribe for new songs every week. Serie de música para mejorar la concentración en el estudio y el trabajo del profesor Manoel Gadi. Especial para personas con TDAH. Suscríbete para recibir nuevas canciones cada semana.
Concentrating to memorize rap
มุมมอง 314 วันที่ผ่านมา
Series of Music to Enhance Concentration for Studying and Working by Prof. Manoel Gadi. Special for ADHD people. Subscribe for new songs every week. Serie de música para mejorar la concentración en el estudio y el trabajo del profesor Manoel Gadi. Especial para personas con TDAH. Suscríbete para recibir nuevas canciones cada semana.
The Sexiest Job of the 21st Century: Data Scientist - Business Analytics - AI Professor (part 1)
มุมมอง 6114 วันที่ผ่านมา
This video is an introduction to the world of analytics and big data. The video discusses the importance of data science and the growing demand for data scientists. It also explores the different types of models used in analytics, such as rule models, formula models, tree models, and graph models. Additionally, the video delves into the concept of big data and how it is changing the way compani...
Unraveling the Mysteries of Decision Trees explained by a human
มุมมอง 614 วันที่ผ่านมา
This video is about the machine learning models known as Decision Trees. The video explains Decision trees, a type of supervised learning model that can be used for both classification and regression tasks. Decision trees work by creating a tree-like model of decisions and their possible consequences. The video explains that decision trees are a good choice for problems where the target variabl...
Extracción de Datos Web (Escrapeo) de Yahoo Finance Key-Statistics con Beautiful Soup
มุมมอง 5921 วันที่ผ่านมา
Se utiliza Beautiful Soup para extraer información específica de la página web de Yahoo Finance pestaña Key-Statistics. Se identifica la estructura HTML de la página y se extraen los datos relevantes, como el nombre de la empresa, el market cap, etc. Se muestra cómo construir un DataFrame para almacenar los datos extraídos y cómo exportarlo a un archivo Excel. El video destaca la importancia de...
Quick intro into R Tidyverse packages
มุมมอง 221 วันที่ผ่านมา
A quick straight to the point intro into to R ecosystem of Tidyverse and Tidymodels, what is it and its components #rprogramming #R #tidyverse #tidymodels
The Sexiest Job of the 21st Century: Data Scientist - Business Analytics - AI Professor (part 2)
มุมมอง 14021 วันที่ผ่านมา
This video is an introduction to the world of analytics and big data. The video discusses the importance of data science and the growing demand for data scientists. It also explores the different types of models used in analytics, such as rule models, formula models, tree models, and graph models. Additionally, the video delves into the concept of big data and how it is changing the way compani...
Estructura de un HTML y una Tabla en HTML
มุมมอง 621 วันที่ผ่านมา
El video describe la estructura básica de un HTML y de una una tabla en HTML, identificando las etiquetas head, body, table, tr y td. #html #table
Extracción de Datos Web (Escrapeo) con Beautiful Soup - Una Introducción
มุมมอง 1221 วันที่ผ่านมา
El video explica cómo extraer datos de una página web utilizando la librería Beautiful Soup en Python. Se enfoca en dos ejemplos principales: Extracción de datos de una tabla HTML: Se utiliza una tabla, identificando con etiquetas table, tr y td. Se utiliza Beautiful Soup para navegar por el DOM (Document Object Model) y extraer el contenido de cada celda de la tabla. Se muestra cómo construir ...
Extracción de Datos Web con Beautiful Soup - De cero a Heroe
มุมมอง 12421 วันที่ผ่านมา
El video explica cómo extraer datos de una página web utilizando la librería Beautiful Soup en Python. Se enfoca en dos ejemplos principales: Extracción de datos de una tabla HTML: Se analiza la estructura HTML de una tabla, identificando las etiquetas table, tr y td. Se utiliza Beautiful Soup para navegar por el DOM (Document Object Model) y extraer el contenido de cada celda de la tabla. Se m...
Discover the Top R Packages for Data Science in 2025!
มุมมอง 2221 วันที่ผ่านมา
Dive into the world of R and learn about the essential packages that every data scientist should know. From data manipulation and visualization to statistical modeling and machine learning, this video covers it all. Key packages explored: dplyr: The ultimate data manipulation tool tidyverse: A powerful collection of data science packages wildcard: Flexible pattern matching for data analysis pas...
Skimr - R Programming - summary function on steroids
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Skimr - R Programming - summary function on steroids
Low-Tech, High-Impact - Technology Toolkit - Startup Buldiing - Innovation Engineering Project
มุมมอง 19321 วันที่ผ่านมา
Low-Tech, High-Impact - Technology Toolkit - Startup Buldiing - Innovation Engineering Project
Intro to the Capstone, PhD, TFM, or TFG channel list
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Intro to the Capstone, PhD, TFM, or TFG channel list
Genetic Algorithms: A Powerful Tool for Machine Learning
มุมมอง 442หลายเดือนก่อน
Genetic Algorithms: A Powerful Tool for Machine Learning
Spyder Crashing when using input() function? Here is how to fix it!
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Spyder Crashing when using input() function? Here is how to fix it!
Nubank Case Case Study - what you should know about it business model!
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Nubank Case Case Study - what you should know about it business model!
Hyperparameter Tuning with Python: Grid Search vs. Random Search? Which is Better?
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Hyperparameter Tuning with Python: Grid Search vs. Random Search? Which is Better?
Download a Bootstrap template adapt and upload it to PythonAnywhere
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Download a Bootstrap template adapt and upload it to PythonAnywhere
Great introductory video prof. Gadi..
¡Qué interesante video! Me encanta cómo Beautiful Soup facilita la extracción de datos. Una vez intenté algo similar con una página de recetas y fue todo un reto 😁. Por cierto, estoy aprendiendo en сodigо hеroe y me está gustando mucho. 🙌🏻
¡Me alegra que te guste Beautiful Soup! He tenido mis batallas con escrapeo, esta semana mismo he conseguido escrapear datos de mi propio canal de TH-cam con con Beautiful Soup, pero algunas cosas han tenido que ser con Regex. Estoy finalizando una serie de videos sobre técnicas avanzadas de web scraping. Suscríbete a mi canal para ser de los primeros en verlos y aprender a extraer datos de cualquier sitio web.
Hello! Traditional risk ratings are generally outdated, and there are countries that still have this kind of data management, not even having everything digitalized. How would risk analysis systems adapt on macroeconomic scale when it needs to analyze data which may be incomplete or outdated? I feel like this would be a nightmare to implement in developing nations where there are instances in which some institutions are very efficient and digitalized while others are decades behind. With the geopolitical landscape becoming much more unstable recently (think of the current South Korean crisis) where the outlook of a country can suddenly change, how would we go around tunning risk systems to analize and correctly assess these fast changes?
Thanks for the interesting insights! In your opinion, when companies implement AI monitoring systems for risk management, who should be held accountable if the system fails and causes significant damage- the developers, the users, or the AI itself?
Professor, I found the idea of AI monitoring other AI systems very interesting. Could this create more complexity or new risks? How should companies test these systems to make sure they work well under pressure?
Hello Professor While watching your video a couple of questions came to my mind. Traditional market risk models assume certain behaviors in market participants. How should these models evolve to account for the unpredictability of AI-driven trading algorithms? Also what mechanisms could central banks or regulators deploy to stabilize the situation after such an event in the future (besides shutting down the trading of stocks)?
Interesting point, traditional market risk models often rely on historical data and statistical assumptions, which may not fully capture the complex dynamics of AI-driven trading. To address this, we need to explore advanced techniques like machine learning and agent-based modeling to better understand and predict the behavior of these algorithms. Additionally, incorporating stress tests that simulate extreme market conditions, including those driven by AI, can help identify potential vulnerabilities and inform risk management strategies. it will be interesting to discuss here and with my students how to to mitigate these risks by considering a multi-faceted approach. This might involve implementing circuit breakers that automatically pause trading when markets experience significant volatility, enhancing surveillance capabilities to detect and respond to anomalous trading patterns, and promoting transparency in algorithmic trading practices. Furthermore, fostering international cooperation and coordination among regulators is crucial to address the global nature of financial markets and the potential for cross-border contagion effects. what do you think?
@manoelfernandoalonsogadi9040 Thank you for your comment Professor. I will come back to this in the future after spending some more time understanding the matter and visiting your classes as I don't think I can give a qualified response yet. But I'm sure that through our course work and projects, I will very soon be able to formulate a good reply:)
Thanks for the video Professor!!! What strategies can organizations adopt to identify and mitigate operational risks associated with the deployment of AI systems, particularly in scenarios where system failures could have significant consequences?
Interesting point, it will be interesting to discuss here and with my students how to to mitigate these risks by conducting thorough risk assessments, developing robust governance processes, and investing in AI security. We could also dicuss the role of central banks and regulators and how they can stabilize markets by providing liquidity, implementing circuit breakers, and intervening in the market. what do you think?
Thank you for the video! As you highlighted the risks of over-reliance on AI in risk management, I wonder about its implications in the context of emerging market trends, particularly in Asia. Given the digital transformation in countries like China, India, and Southeast Asia, AI-driven financial systems are being adopted at an unprecedented pace. How can we balance the need for speed and accuracy with the risk of systemic errors that could ripple through these highly interconnected markets? Additionally, considering the region's unique regulatory environments and geopolitical dynamics, do you see specific challenges or opportunities for developing robust AI frameworks that can mitigate these risks while fostering innovation?
Interesting point of view. It will be interesting to discuss here and with my students how to to mitigate these risks, developing robust AI frameworks, and also, how it could be tailored to the region's specific regulatory environments and geopolitical dynamics. The rapid adoption of AI-driven financial systems in countries like China, India, and Southeast Asia presents a unique challenge. Balancing the need for speed and accuracy with the risk of systemic errors is crucial. what do you think?
Love this series. I would love to see more African rithms.
I think Nubank has a great opportunity to disrupt the SME lending market in Brazil and beyond.
Given the project I will be working on concerns the field of financial and credit risk, I am particularly interested in: 1. How can AI be helpful in assisting credit rating agencies in assigning the correct credit ratings and/or investors in accurately assessing the risk of default of debt instruments such as bonds and ABS as well as structured products such as CDOs, CLOs, and CDS? 2. Given the multi-layered complexity of structured financial instruments such as CDOs and CLOs, is the potential of AI in improving the credit ratings for these types of instruments significantly higher than e.g., for plain vanilla bonds? 3. How can AI-based systems help rating agencies and institutional investors in detecting early warning signs of potential defaults? Any insights into any of the above questions would be greatly appreciated.
Very good point, It will be interesting to discuss here and with my students on how Moody's is doing this in the project: Moody's Research Assistant - www.moodys.com/web/en/us/research-assistant.html what do you think?
Sensacional, muito criativo e amor envolvido.
Thanks for this, clearly and simply demonstrated.
I am glad you liked, subscribe to the channel, more stat content coming.
I am interested in financial and credit risk. For instance, it is very intriguing how predictive models help forecast market shifts which assists in mitigating financial risk. Yet, I would like to know your opinion on the use of AI and ML in real-time decision making especially in volatile market conditions.
Difficult point, It will be interesting to discuss here and with my students on how to mitigate this proposing robust oversight and monitoring. Do you have anything else in mind?
Thank you for your insights Professor; I'm interested in financial and credit risk, and I'd love to hear your thoughts on how banks use machine learning to improve credit risk assessments, and what challenges they face in ensuring these models are fair and unbiased?
Difficult point, It will be interesting to discuss here and with my students on how to mitigate this by using tools like fairdetect (mentioned in th-cam.com/video/kY0GUQ5bk-0/w-d-xo.html and on GitHub at github.com/manoelgadi/fairdetect), which is a research project trying to identify potential biases in the data or model outputs, promoting fairer credit decisions. Do you have anything else in mind?
What is the best was to learn Python and Pandas
I would recommend you to watch my video on the making of a pandas dataframe to really understand what is under the hood: th-cam.com/video/aTMj40Un_JA/w-d-xo.html
I’m particularly interested in learning more about operational and technology risks, especially in today’s world where technology is related mostly to AI-driven systems. As we embrace AI in risk management, understanding how to balance it with robust risk control and regulations is important. Goal would be to ensure AI models remain transparent and resilient to errors in high-stakes scenarios.
Thanks for your comment! I'm glad you're interested in learning more about operational and technology risks in AI-driven systems. It's a critical topic as AI becomes more prevalent in all aspects of our lives. You sound knowledgable in the topic, so I'd love to hear your thoughts on the following: - What do you see as the biggest challenges in managing operational and technology risks nowadays? - What do you see as the biggest challenges in managing operational and technology risks in AI-driven systems? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below.
@@phdmanoelgadi Thank you for the reply, in today’s world, the biggest challenge in managing operational and technology risks is to be able to ensure adaptability to evolving and fast paced regulations. For AI driven systems, it is important to balance transparency, bias mitigation and resilience while keeping it ethical.
I would like to come back to the idea of rules and monitoring systems with AI that we can apply to Market Risk and Country Risk. If we think about creating new frameworks for risk with rules, basic or AI monitoring, we need to look at who would be creating this framework. In regions such as the EU, there is an entity more important than the country itself that creates rules, allowing to have regulations for the markets and countries in that area. Other countries would / are creating these rules by themselves for their own country and market. This can bring ethics questions into play, creating advantages for a market or country.
Thanks for your comment! You raise an excellent point about the ethical considerations surrounding the creation and implementation of risk frameworks with AI. It's true that different regions have varying regulatory approaches, which can lead to potential advantages or disadvantages for certain markets or countries. You sound knowledgeable in the topic, so I'd love to hear your thoughts on the following: - What do you see as the biggest challenges in managing Market Risk risks nowadays? - What do you see as the biggest challenges in managing Market Risk in AI-driven systems? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below.
@@phdmanoelgadi Thank you for your answer, I would love to develop my thoughts on this topic. While in Europe there is the EU AI Act assuring homogeneity in AI use in financial market, there is no federal law for the United States. Indeed each state has specific rules that regulate automated decision making. If we look at the United State as one Market, some states can have advantages and disadvantages regarding how they use AI. One example is how some companies might decide to change their headquarters based on the state rules. This may benefit the state and bring economical value to the state. Another examples is how customers may also be impacted by AI rules from other states. For example privacy regulations might be different and a customer from a state that purchases stock in the same market from another state might have to do research to make sure the state rules or the companies follows his or her values or needs. Or in another case, the customer might not be aware of those different regulations.
Operational risks in AI systems, like performance issues or failures, demand robust recovery strategies. Real-time solutions such as fallback algorithms or redundant infrastructure can ensure continuity in high-stakes scenarios. However, balancing these with pre-deployment stress testing and oversight is crucial. Are current frameworks sufficient, or do they need innovation to handle AI complexities? Should operational risk frameworks include automated fail-safes, such as AI "kill switches," to shut down systems in real time during anomalies?
Thanks for your comment! Operational risks in AI systems are a significant concern, especially as AI becomes increasingly integrated into critical infrastructure and decision-making processes. While current frameworks offer some solutions, there's a need for innovation to address the unique challenges posed by AI. The list of important topis to discuss goes on: Robustness and Redundancy, Pre-Deployment Stress Testing, Ongoing Monitoring and Oversight, Automated Fail-Safes... You sound knowledgeable in the topic, so I'd love to hear your thoughts on the following: - What do you see as the biggest challenges in managing Operational risks nowadays? - What do you see as the biggest challenges in managing Operational risks in AI-driven systems? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below.
I am also interested in market and country risk. I want to learn more about how AI can effectively predict the timing and magnitude of central bank interventions, and how reliable are these models in volatile conditions?
Thanks for your comment! The reliability of AI models in volatile conditions is a significant concern like the wars and economic wars we are seeing. The list of important topis to discuss goes on: Model Managment, Robust Model Validation, Continuous Monitoring and Adaptation, Human Oversight... You sound knowledgeable in the topic, so I'd love to hear your thoughts on the following: - What do you see as the biggest challenges in managing Market and Country risk nowadays? - What do you see as the biggest challenges in managing Market and Country risk in AI-driven systems? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below.
I am interested in further discussing market and country risk. How can we factor in geopolitical risk in international economic and political relationships?
Thanks for your comment! The reliability of AI models in volatile conditions is a significant concern like the wars and economic wars we are seeing. The list of important topis to discuss goes on: Model Managment, Robust Model Validation, Continuous Monitoring and Adaptation, Human Oversight... You sound knowledgeable in the topic, so I'd love to hear your thoughts on the following: - What do you see as the biggest challenges in managing Market and Country risk nowadays? - What do you see as the biggest challenges in managing Market and Country risk in AI-driven systems? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below.
@@phdmanoelgadi Thank you very much for your response. 1st question: - Trade agreements (recently seen hiking tariff policies presented by Trump) -- Can AI help with managing and generating mutually beneficial trade agreements between nations/member states? - Securitization - How much should a country invest in their military? 2nd question: - Reliability of information. We have recently seen the ease of fabricating data that can be used for propaganda - Data governance. Who is the owner of data? Is it owned by the prompt engineers in the case of AI generated trade/government policies, or the providers of these services?
This video provided valuable insights into leveraging AI for Financial & Credit Risk management. Professor Gadi’s emphasis on balancing AI benefits with risks aligns perfectly with my research focus.
Thanks for your comment! I'm glad you found the video helpful. It's important to strike a balance between the potential benefits of AI and the risks associated with its implementation. I'd love to hear your thoughts on the following: What specific AI techniques or applications do you believe hold the most promise for improving financial and credit risk management? How can we ensure that AI models are transparent, explainable, and fair, especially in high-stakes decision-making? I'm also interested in hearing from others in the community. If you have any thoughts or questions on this topic, please feel free to share them below. Thanks, Manoel Gadi
tq so so so soooo much... this vedio fix my issue .. thanks for making this vedio.. it took me nearly half day to fix the issue
I'm glad the video could help you resolve your issue! It's always satisfying to know that the information we share can be useful. If you have any other questions or need further assistance, feel free to ask. Please subscribe and like the video to help the channel.
I'm glad the video could help you. If you have any questions or need further assistance, feel free to ask or recommend more topics I could cover on future videos. Please subscribe and like the video to help the channel.
nice👍
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Excellent Thanks for your help!
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Thanks! I learned a new trick today!!
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is there a way to go back and debug the last line again?
Unfortunately, Spyder doesn't directly allow you to go back and re-execute a specific line of code during debugging. You can use pythontutor website if your code is not very complex. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Thank you so much, I tried to find how to go inside a function when debugging for ages !
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Thank you for sharing your work. I love the completeness of an app to front-end development
I'm glad the video could help you. If you have any questions or need further assistance, feel free to ask or recommend more topics I could cover on future videos. Please subscribe and like the video to help the channel.
Thank you I fixed my code after fighting it for 3 hours :D
Glad it helped! Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
nice work Manoel, can you please give me access to your code.
It is at my github, the code I start the video with is: github.com/manoelgadi/CompanySearcher_start and the code generated during the video is here: github.com/manoelgadi/CompanySearcher_final
Thanks a lot <3
Thanks. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
It was very useful. Thank you!
Glad it was helpful!
Looked clear and simple, but I'm with a later version: 5.1.5 It looks like this, but the Icons are not quite the same. That should not be a problem. However nothing works as you show above. I use exactly the same code. I try to run in "debug mode" with the left most blue icon I just get this: IPython 8.7.0 -- An enhanced Interactive Python. debugfile('D:/devt/Python3/debugger_tests.py', wdir='D:/devt/Python3') > d:\devt\python3\debugger_tests.py(2)<module>() 1 # -*- coding: utf-8 -*- ----> 2 """ 3 Created on Thu Jan 26 16:36:32 2023 4 5 @author: larry Then the debugger just hangs, no button clicking does anything.
I looked up the problem with a search. The advice was to reinstall Anaconda entirely. There seems to have been a sequence of Spyder versions where the debugger did not work. Well, the latest Anaconda fixes that problem.
Thanks for watching the tutorial! While the core debugging functionality remains the same across different Spyder versions, you might be facing a version-specific bug. I am glad that reinstalling Anaconda solved the issue. For more people reading here, one option should be: Upgrading Spyder to the latest version should resolve the issue. Open Anaconda Prompt, type `conda update spyder`, and press Enter. This should update Spyder within your current Anaconda environment. Try debugging your code again, and it should work as expected. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Thank you!
You're welcome!
How to disable debuge
To disable debugging in Spyder 4, click the "Stop Debugging" button in the toolbar or use the Ctrl+Shift+F12 shortcut to stop the current debugging session. To prevent automatic debugging on errors, go to Tools -> Preferences -> Run -> General settings and uncheck "Directly enter debugging when errors appear." I looked up the problem with a search. The advice was to reinstall Anaconda entirely. There seems to have been a sequence of Spyder versions where the debugger did not work. Well, the latest Anaconda fixes that problem.
Sir isme graphic ke coding run nahi ho rahi he jaise shinchan cartoon
While the core debugging functionality remains the same across different Spyder versions, you might be facing a version-specific bug. One option you have is to reinstal Anaconda as a whole, but first tryi upgrading it: Upgrading Spyder to the latest version should resolve the issue. Open Anaconda Prompt, type `conda update spyder`, and press Enter. This should update Spyder within your current Anaconda environment. Try debugging your code again, and it should work as expected. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
thank you
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Thank you very much Manoel! I now can move from google colab to jupyter notebook to use autosklearn for my master thesis. Good job, bro!
You are welcome! Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Those blue buttons don't work at my Spyder 3.7 version ....
While the core debugging functionality remains the same across different Spyder versions, you might be facing a version-specific bug. One option you have is to reinstal Anaconda as a whole, but first tryi upgrading it: Upgrading Spyder to the latest version should resolve the issue. Open Anaconda Prompt, type `conda update spyder`, and press Enter. This should update Spyder within your current Anaconda environment. Try debugging your code again, and it should work as expected. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
increible
KKK
Jjjjajajajajaja que grande tio
kkkkk
Exhausting. Not a good experience. :(
While the core debugging functionality remains the same across different Spyder versions, you might be facing a version-specific bug. One option you have is to reinstal Anaconda as a whole, but first tryi upgrading it: Upgrading Spyder to the latest version should resolve the issue. Open Anaconda Prompt, type `conda update spyder`, and press Enter. This should update Spyder within your current Anaconda environment. Try debugging your code again, and it should work as expected. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Thank you. Clearly explained!
You're welcome!
Thank you Sir!!!
You are welcome. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Thanks very much. Short concise video and to the point.
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Thank you
You are welcome. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!
Thank you. Very clear.
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Thank you. Your explanation is great! Exactly what I needed to know. You deserve more views.
You are welcome. Please subscribe to see the upcoming content and make sugestions of more content you'd like to see in the channel! Thanks!