This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
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Both logistic regression and T -test is used when one variable is continuous and other categorical? How to decide ?
Thank you Sir. Can you make a video on how to train our own ML model based on a CSV data (to predict phenomena, like intrusion activities), and integrate it with an LLM API to generate an alarm if there is some intrusion activities predicted by the trained ML model?
superb man , very easily understood. appreciate it
Please share the code with annual data.There are lot of codes on monthly data but I am searching annual data.
Just Perfect! BUT, Aman! Please make a lecture on how to train our own machine learning model based on a CSV dataset (classification), run it on the Huggingface/AWS-cloud, and integrate it with the LLMs (probably using RAG for best response). I want to create an alerting system in which the driver should be alerted if he approaches to an accident-prone area (based on the prediction of the ML model and the live location of the driver, possibly received from google map API). I think many APIs should be integrated, such as Google Maps for providing live stream of data and the road conditions, the Fast API/Flask, and many more that you know better. Thank you!
You are right Aman. Same here in Germany. I think west Europe follows the same pattern. It’s a bit different than India
good bhaiyya
sir...are you teaching Machine Learning. If so can you please direct me to the link
Excellent, to the point, good examples. Great work!
thanks for the video
how to use DBSCAN in case of multiple features? Is there any technique to use only few features or all feature but less important with very small weightage?
Wow I have understood APIS now... Thanks very much. However am requesting you to make a broader video explaining the second concept of using payload as it is more complex. Thanks in advance. Please tag me when you make the video.
Just watched it before exam and in one go ,i understood the concept ❤keep making such videos sir 🥰🥰
Excellent impartation
These mock interviews are very useful. Please make more like this
Sampling is the process of getting sample from the population
Hi aman, this is simply superb, could you please do a video on complete end to end RaG with Agents, Thank you
Hi your content was very good and helpful. I need one help can please upload a one end to end LLM based project with open source and also please use supabase for this db on csv data or any kind of structured and unstructured data please 🙏 Thank you
Very clear, thank you sir!
dataset?
the way you explained is very clear and comprehensible. Thank you brother
Is there any part 2 also of this playlist??
Nice I need this play list basic you generate a good Contant
Sir salary for one month
what is the cost of deploying a model in amazon sagemaker?
Sir what about your com skills
Many youtubers learn from your video and explain in hindi. And they are famous 😅😅
Good effort keep it up please
Very useful 👍
Excellent sir 🎉
Thank you for this
Very helpful
0:27 check your subtitle in hindi
Outstanding lecture! Love it. Thanks very much
Aman tell me one thing on when to use which technique of feature selection
Excellent
I'm glad i found a teacher like you
Thanks
Nice work. Keep up. We need the video on encorder-decorder etc
Amazing video! How did you work out the slope value to be -4?
What the results tell or what it denote
Good broh
Thanks vaiya😊
If we have more than 2 classes in categorical variables, for eg, 2 classes in one variable and 3 classes in another variable, then also chi square can be used ? Please help sir.
Verywell explained but title should be changed to opeai. RAG is explained greatly!