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Machine Learning Tutorial Python | Machine Learning For Beginners

Machine Learning Tutorial Python | Machine Learning For Beginners

$9.99

$109.99

The "Machine Learning Tutorial Python | Machine Learning For Beginner" course offers an accessible introduction to machine learning using Python. It covers essential topics such as data preprocessing, supervised and unsupervised learning, and model evaluation, using popular libraries like scikit-learn and pandas. Designed for beginners, this course provides practical exercises and real-world examples to help you build, train, and evaluate machine learning models, equipping you with the skills needed to apply machine learning techniques effectively in various scenarios.

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Has discount
Expiry period Lifetime
Made in English
Last updated at Mon Jul 2024
Level
Beginner
Total lectures 42
Total quizzes 0
Total duration 12:00:43 Hours
Total enrolment 194
Number of reviews 38
Avg rating
Short description The "Machine Learning Tutorial Python | Machine Learning For Beginner" course offers an accessible introduction to machine learning using Python. It covers essential topics such as data preprocessing, supervised and unsupervised learning, and model evaluation, using popular libraries like scikit-learn and pandas. Designed for beginners, this course provides practical exercises and real-world examples to help you build, train, and evaluate machine learning models, equipping you with the skills needed to apply machine learning techniques effectively in various scenarios.
Outcomes
  • Proficiency in Machine Learning Basics: Gain a solid understanding of core machine learning concepts and algorithms.
  • Effective Data Preprocessing: Apply techniques for data cleaning, transformation, and preparation.
  • Model Building Skills: Build, train, and evaluate machine learning models using Python tools and libraries.
  • Application of Supervised Learning: Implement and interpret supervised learning algorithms for regression and classification tasks.
  • Knowledge of Unsupervised Learning: Utilize unsupervised learning methods to perform clustering and dimensionality reduction.
Requirements