good course , some part is typical more statistical part shown, even i have good understanding of ML , so new learner will find little typical. rest tutor voice and language is understandable.
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
551,053 already enrolled
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4.7
(17,341 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
Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.
Skills you'll gain
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Taught in English
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Learner reviews
4.7
17,341 reviews
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3.39%
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Showing 3 of 17341
RV
5
Reviewed on Jan 15, 2025
CA
4
Reviewed on Jan 1, 2020
could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice
JT
5
Reviewed on Apr 18, 2020
This course was a great taster for machine learning techniques. My only recommendation would be to add more explanation on tuning techniques for models and cover more of the supporting mathematics.
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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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