I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
548,673 already enrolled
Included with 
4.7
(17,324 reviews)
Intermediate level
Recommended experience
1 hour to complete
Flexible schedule
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.
Skills you'll gain
Details to know
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Taught in English
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Learner reviews
4.7
17,324 reviews
- 5 stars
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18.64%
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3.39%
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0.97%
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Showing 3 of 17324
FO
5
Reviewed on Oct 9, 2020
HR
5
Reviewed on Sep 6, 2021
This course just gave me substantial and valuable knowledge on ml algorithms and how to apply it using python. As a result, it was a benefiting journey for me. Thanks a lot for such a course.
RN
5
Reviewed on May 26, 2020
Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
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Frequently asked questions
Python’s popularity in machine learning stems from its simplicity, readability, and extensive libraries like TensorFlow, PyTorch, and scikit-learn, which streamline complex ML tasks. Its active community and ease of integration with other languages and tools also make Python an ideal choice for ML.
Machine learning engineers use Python to develop algorithms, preprocess data, train models, and analyze results. With Python’s rich libraries and frameworks, they can experiment with various models, optimize performance, and deploy applications efficiently.
Python offers a wide range of ML libraries, is beginner-friendly, and has great support for data visualization and model interpretation. It also supports rapid prototyping, making it easier to test and refine models compared to other languages like C++ or Java.
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