Analytics-University
The video discusses the new chat board launched by OpenAI, ChatGPT, which is a beneficial tool for data scientists learning Python. ChatGPT saves time by providing instant answers to questions about syntax errors and finding the correct syntax for different tasks in Python. Additionally, the chatbot demonstrates its ability to provide specific solutions to data science queries, such as how to run stepwise logistic regression, using Python libraries such as stats model and SK learn, which would be a time-consuming process if one were to use Google search. The benefits of using ChatGPT to quickly get answers to any confusion or questions about Python are highlighted, ultimately saving valuable time.
In this section, the speaker discusses the limitations of using Google search to find answers to complex questions in the field of data science, as it often requires scanning through multiple articles and using one's own understanding to determine the correct answer. They then introduce the ChatGPT chat board, which was just launched by OpenAI and trained on data only up until 2021. The speaker explains how to use ChatGPT by first creating an account, and how it can be beneficial for data scientists who may have coding queries and require instant answers without having to ask someone else.
In this section of the video, the speaker explains the benefit of using Chat GPT when learning Python. Google might give hundreds of articles on syntax errors, but it becomes difficult to figure out exactly what's wrong. Chat GPT tries to see what is wrong with your code and suggests possible solutions. By copying and pasting the code with a question mark, it will give suggestions on the syntax error and provide an explanation. Additionally, Chat GPT can provide information on different ways to create arrays in Python or specific libraries like NumPy. Overall, Chat GPT can save learners a lot of time in figuring out syntax errors and finding the correct syntax for different tasks in Python.
In this section of the video, the chatbot demonstrates its ability to provide tabular format answers to the difference between List and Tuple in Python. The chatbot also showcases how it can provide specific solutions to data science queries, such as how to run stepwise logistic regression, using Python libraries such as stats model and SK learn, which would be a time-consuming process if one were to use Google search.
In this section, the speaker discusses the use of Logistics and model selection from SQL feature selection to perform stabilization on SK loan, despite the fact that it does not have a built-in function for stabilization. They go on to explain the benefits of using Chat GPT to quickly and easily get answers to any confusion or questions one may have about Python, such as how to calculate a confusion matrix or create arrays and data frames, ultimately saving valuable time.
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