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DTSTART;TZID=America/Los_Angeles:20260302T190000
DTEND;TZID=America/Los_Angeles:20260302T200000
DTSTAMP:20260420T105045
CREATED:20260303T041846Z
LAST-MODIFIED:20260303T041916Z
UID:32729-1772478000-1772481600@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nContinued multi-class classification on the MNIST dataset.\nLearned about data augmentation through the batch_tfms parameter for the dataloader.\n\nHomework:\nFor Reine: \n\nCreate a google doc under your homework google drive \nWrite up your project proposal for training your own AI model detailing:\n– What you will be classifying (has to be multi-class classification!)\n– Where your data comes from (provide a link or methodology of getting the dataset). If you are using an existing dataset\, you have to add more images from online means (gather data from outside of kaggle\, whether it’s online images\, etc)\n– How you will train your AI model (what transformations on the data you will use\, and the flow of training)\nYou can view the list of transformations here: https://docs.fast.ai/vision.augment.html#aug_transforms\n\nEnsure the google doc is public access and that it is in your homework folder. \n  \nFor everyone: \n\nContinue working on your final project. You want to at least ensure you have the data loader setup with the proper transformations (also at this point it should be easy to run training once you have the dataloader). Upload your latest progress as an ipynb to the google drive.\nCreate a new kaggle notebook Mar9_PandasHW.ipynb and do the following:\nYoutube.csv  \n\nUse this csv file – https://drive.google.com/file/d/1kP6A9y0UBssOg3Exunv9Mnmilb0657Sh/view?usp=drive_link \nLoad the data\n\nShow only the Channel and Subscribers columns\n\nFind channels with more than 2000 subscribers\n\nAdd a column Subs_per_Video\n\nWhich channel is the most efficient?\n\n\n\nHW Note: you can display rows with conditions like this:\ndf_math_greater_than_80 = df[df["Math"] > 80]\nprint("\nStudents with Math Score Greater Than 80:")\nprint(df_math_greater_than_80)\nprint(df.head())\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-9/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T190000
DTEND;TZID=America/Los_Angeles:20260304T200000
DTSTAMP:20260420T105045
CREATED:20260305T062853Z
LAST-MODIFIED:20260305T063006Z
UID:32754-1772650800-1772654400@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nIntroduced the topic of ML/AI at a high level.\nIntroduced Kaggle as a platform for running ML code.\n\nHomework:\nCopy the contents from this google colab file to kaggle\, and complete the hw\, then save as .ipynb: https://colab.research.google.com/drive/1qjmmP4LhSIgVD0qmExb_fmuB-Sm_511n \nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-10/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T190000
DTEND;TZID=America/Los_Angeles:20260309T200000
DTSTAMP:20260420T105045
CREATED:20260310T033604Z
LAST-MODIFIED:20260310T035916Z
UID:32805-1773082800-1773086400@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\nReviewed using pandas\nStarted the Titanic Survival Rate Prediction Problem\n\nHomework:\nFor everyone: \n\nContinue working on your final project. You want to finish training your model! Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-11/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260311T190000
DTEND;TZID=America/Los_Angeles:20260311T200000
DTSTAMP:20260420T105045
CREATED:20260312T031009Z
LAST-MODIFIED:20260312T031009Z
UID:32837-1773255600-1773259200@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nIntroduced the training pipeline for Supervised Learning at a high level.\nIntroduced Kaggle as a platform for running ML code.\n\nHomework:\nIn your wed-mar4-catvsdog kaggle notebook\, add in 25 cat images and 25 dog images to a new dataset named “CatVSDog_Test_Images“. These should be random and not duplicates of one another. You may use photos of your own cat/dog if you have one. \nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-12/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260318T190000
DTEND;TZID=America/Los_Angeles:20260318T200000
DTSTAMP:20260420T105045
CREATED:20260320T011814Z
LAST-MODIFIED:20260320T011814Z
UID:32915-1773860400-1773864000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nContinued the first project on Cat vs Dog classification\nLearned more on how training an AI model in a supervised way works.\n\nHomework:\nIn your wed-mar4-catvsdog kaggle notebook\, do the following: \n# hw: \n# Ensure your individual custom dataset images have capitalization of the first letter of the file name\n\n# run prediction on each image in ur custom dataset then\nsee if there are any errors\, and print out the number of correct guesses\n\n# you can use the labeling function to test correctness.\nso something like:\nif predicted_ans == true_ans:\n   corrects += 1\n\n# hint: run a for loop over the list of your custom images\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-13/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260323T190000
DTEND;TZID=America/Los_Angeles:20260323T200000
DTSTAMP:20260420T105045
CREATED:20260323T215733Z
LAST-MODIFIED:20260324T031751Z
UID:32953-1774292400-1774296000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\nContinued the Titanic Survival Rate Prediction Problem\n\nHomework:\nFor everyone: \n\nContinue working on your final project. You should have a model trained already at this point\, and you want to get streamlit up and running for testing model deployment! Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \n  \nAlso in the latest titanic surival project\, jot down one observation you have on what the model learned or some trend in the data (for example in class we observed that higher ages lead to lower survival rates) \nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-14/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260330T190000
DTEND;TZID=America/Los_Angeles:20260330T200000
DTSTAMP:20260420T105045
CREATED:20260330T235041Z
LAST-MODIFIED:20260331T062753Z
UID:33029-1774897200-1774900800@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\, debugged streamlit and dataset related issues.\nFinished the Titanic Survival Rate Prediction Problem!\n\nHomework:\nPart 1:\n\nContinue working on your final project.\nYour goal this week is to train multiple models and deploy them all with the option to choose between different models on streamlit. Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \nPart 2:\n\nAdd some code in the Titanic Survival Rate project.\n\nCount how many passengers are male and how many are female. Use “Sex” column.\nFigure out the most expensive ticket price and the cheapest and the average. Use “Fare” column.\nFigure out how many people are younger than 10 years old. Use “Age” column.\n\n\n\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-16/
CATEGORIES:AI/ML,Python Class
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