This is machine learning foundational course. Wherein we have tried to cover the basic machine learning algorithims in depth. For KNN and more advanced algorithms you can search like: 'KNN in Machine Learning Intellipaat' and you will find the contents for the same created by our team. Hope this helps!!!
Logistic regression is best suited for binary classification problems with linear relationships, while decision trees work well with non-linear relationships and can handle both categorical and continuous data. If the data is complex, decision trees may offer better performance. However, it's a good idea to try both and compare their results to see which works better for your specific problem.
2:43:20 - I fully believe and trust you on this one already. You pour your whole heart out for this! And you are so natural as well.
3:34:40 yes, because the predicted prices align closely with the actual prices when the gap is smaller between these two lines.
You're amazing! You make machine learning so easy to understand. Thank you so much.
That's awesome to hear! Glad you're enjoying the course.
Nunca um Indiano mas sempre um indiano ensinando o que o povo não quer que a gente aprenda
Thank you so much. Great explanation easy to understand for beginners
I'm glad you found it helpful!
What about knn algorithm and others
This is machine learning foundational course. Wherein we have tried to cover the basic machine learning algorithims in depth. For KNN and more advanced algorithms you can search like: 'KNN in Machine Learning Intellipaat' and you will find the contents for the same created by our team. Hope this helps!!!
How can we know when to use the logistic regression or the decision tree classification or which is better 9:51:11
Logistic regression is best suited for binary classification problems with linear relationships, while decision trees work well with non-linear relationships and can handle both categorical and continuous data. If the data is complex, decision trees may offer better performance. However, it's a good idea to try both and compare their results to see which works better for your specific problem.
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