Ever since I came across your videos, I can't stop watching. I've seen a lot on the internet, but you are the only ones who can present information so clearly. Thank U a lot!
@mucunguzirwakatwe912711 ай бұрын
True, absolutely clear
@kimaegaii10 ай бұрын
Great course. Just a thought, would be nice to add a #4 where you pick one and go through it together with us and slowly. Again, very well done!
@sarthakmalik6688 Жыл бұрын
00:04 Training a machine learning model using scikit-learn 02:15 Scikit-learn provides useful metrics and pre-prepared datasets for machine learning. 04:29 Splitting Data into Train, Test, and Validation Sets 06:49 The main steps in the process are data set sampling, model training, and model selection 08:59 Scikit-learn documentation provides thorough explanations and examples of importing and using different algorithms and models. 11:10 There are multiple ways to evaluate machine learning models 13:25 Creating a confusion matrix and classification report using Seaborn and scikit-learn 15:42 Accuracy alone may not provide a complete picture of model performance. 17:46 GridSearchCV helps create and compare multiple models with different hyperparameters. 19:41 Grid search includes cross-validation for training models with different parameter combinations. 21:44 Get started with scikit-learn: data preparation, model training, and evaluation. Crafted by Merlin AI.
@fernandes1431 Жыл бұрын
So well explained. Thank you!
@AssemblyAI Жыл бұрын
You're very welcome!
@benstallone6784 Жыл бұрын
Not a big deal since you didn't change any default parameters but you implemented a classifier while showing the regression documentation Great high level overview of the ML training process.
@AssemblyAI Жыл бұрын
Thank you!
@metanulski Жыл бұрын
We got the setup, prepared the data, we did the training and the validation. But in the end we never got to use the model. I thought this would be the most important part.
@dennisbunarta1190Ай бұрын
Misra, i have question.. In 11:49 why did you train x and y train in fit() instead of the x and y that you've oversampled before?
@apurbaafiat6012Ай бұрын
Amazing video.
@pamoutaf5 ай бұрын
This is great, thank you!
@kepenge Жыл бұрын
Hi @Misra, I'm using the flights dataset, full as submited in Kaggle, I'm running the clf = RandomForestClassifier(random_state=0) followed by the cross_val_score and it stay for so long time processing and don't finish at least with 1:30 hour. I'm using a MacBook Pro M1 with 64GB , Is there anything that I'm missing?
@adityatiwari3646 Жыл бұрын
Thanks for the explanation mam it helped , i m having a doubt with my project....see as i working on a AI LLM module so I have completed the training the dataset and all that stuff....now i stucked at testing the module I m not getting that how to test your module..... Can you please guide over that....like maybe a proper video describing how to test you ML Models r AI LLM modules.....it will help a lot mam!!....I need it!!.....❤
@smudgepost Жыл бұрын
Not a step by step guide this time, I was able to follow along though the code errors just after In [44] as X_resampled is not defined. Interestingly my seaborn heatmap was mostly dark.
@DanielADamico Жыл бұрын
Great video! And she is the most beautiful programmer I have ever seen ❤
@statusmart1934 Жыл бұрын
Where can I get the cheat sheet for choosing model
@ingluissantana Жыл бұрын
Great Video!!!
@AssemblyAI Жыл бұрын
Thank you!
@EliSpizzichino Жыл бұрын
Well explained however I feel it was a little bit rushed. I'm wondering why the model (shown in the classification_report) is still so unbalanced even after RandomOversamlper did rebalance the classes. Anyway PCA and dimensionality reduction topics are welcomed even to complement this tutorial, given you've skip to encode the other non numerical data that could have provided good insight for the model and provide better AP