Cleaning and Manipulation a Dataset With Python

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-sp19-data-curation").exists():
    DATA_DIR /= "ucfai-core-sp19-data-curation"
    # You'll need to download the data from Kaggle and place it in the `data/`
    #   directory beside this notebook.
    # The data should be here:
    DATA_DIR = Path("data")

Other Meetings in this Series

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Some of the hardest aspects of Machine Learning are the details. Almost every algorithm we use is sensitive to "hyperparameters" which affect the initialization, optimization speed, and even the possibility of becoming accurate. We'll cover the general heuristics you can use to figure out what hyperparameters to use, how to find the optimal ones, what you can do to make models more resilient, and the like. This workshop will be pretty "down-in-the-weeds" but will give you a better intuition about Machine Learning and its shortcomings.

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Neural Nets are not the end all be all of Machine Learning. In this lecture, we will see how a decision tree works, and see how powerful a collection of them can be. From there, we will see how to utilize Random Forests to do digit recognition.

Contributing Authors

John Muchovej
John Muchovej

Founder of AI@UCF. Researcher in cognitive science and machine learning. Focusing on intuitive physics and intuitive psychology.