Udemy - Machine Learning Theory (Basic) NEW

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Udemy - Machine Learning Theory (Basic) NEW (Size: 349.5 MB)
  1 - Data Collection and upload dataset in google colab.mp4 131.6 MB
  2 - 03.Data-Preprocessing-Techniques.pptx 2.5 MB
  2 - Data Preprocessing TechniquesSteps.mp4 57.5 MB
  3 - 04.Feature-Engineering-for-Machine-Learning.pptx 1.1 MB
  3 - Feature Engineering for Machine Learning.mp4 19.4 MB
  4 - 05.Supervised-vs-Unsupervised-vs-Reinforcement-Learning.pptx 298.9 KB
  4 - Supervised vs Unsupervised vs Reinforcement Learning.mp4 30.5 MB
  5 - 06.Handling-missing-values-part-01.pptx 1.8 MB
  5 - Mastering Missing Data Handling.mp4 104.9 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B

Description


Machine Learning Theory (Basic) NEW

https://DevCourseWeb.com

Published 8/2024
Duration: 44m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 349 MB
Genre: eLearning | Language: English

Best Theory Course for ML

What you'll learn
Where to Collect Data For Machine Learning? | Data Collection
Data Preprocessing Techniques/Steps
Feature Engineering for Machine Learning
Supervised vs Unsupervised vs Reinforcement Learning

Requirements
Basic Computer Literacy: Familiarity with using a computer, including browsing the internet, using basic software, and managing files.
Interest in Programming: A genuine interest in learning programming and problem-solving techniques.
Access to a Computer: A personal computer with a stable internet connection to participate in online classes, complete assignments, and practice coding.
Basic Understanding of Mathematics: Knowledge of high school-level mathematics, including algebra, is beneficial for understanding algorithms and data structures.

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