Python is the most popular programming language among data scientists and machine learning developers, with 57% using it and 33% prioritising it for development. It's no surprise, given the rapid evolution of deep learning Python frameworks in the last two years, which has included the release of TensorFlow and a slew of other libraries.
Regularization becomes important when the model begins to underfit or overfit. It's a type of regression that diverts or regularises the coefficient estimates toward zero. To minimise overfitting, it decreases flexibility and discourages learning in a model. The model's complexity decreases, and it improves its prediction ability.
Training labelled data is required for supervised learning. To accomplish classification (a supervised learning task), for example, you must first label the data that will be used to train the model to classify data into your labelled categories. Unsupervised learning, on the other hand, does not necessitate intentional data labelling.
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
5
Amazing Teaching
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Juboraj Juboraj
5
Easy to understand & explain details.
J
Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
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