Distinguish between AI, machine learning, and deep learning, and explore the four main approaches: supervised, unsupervised, semi-supervised, and reinforcement learning.
Before coding, it is crucial to define the problem you are solving and determine if machine learning is the right solution over simpler business rules.
Discover where to find public datasets like Kaggle and use Python to perform an initial exploration of your data to identify issues like missing values.
Learn to clean, transform, and expand your dataset using techniques like feature encoding and data augmentation while avoiding the common pitfall of overfitting.
Properly divide your data into training and testing sets, then get an overview of common algorithms like regression, decision trees, and random forests.
Use tools like the confusion matrix and metrics like mean squared error to assess your model's accuracy and apply techniques for improvement, such as handling outliers.
See how companies like Netflix use machine learning and review the key steps for starting your own ML project, from data collection to model verification.
57:46Adrian Schmitt
35:16Ankit Patel
45:45Simon Stiebellehner
22:11Antonia Hahn
27:19Nura Kawa
44:40Hauke Brammer
56:46Jodie Burchell
44:11Alexandra Waldherr
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Jobs that call for the skills explored in this talk.
Understanding Recruitment Group
Barcelona, Spain
LEONHARD WEISS GmbH & Co. KG
Warmwind