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!
Machine Learning with Python
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Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
639,990 already enrolled
Included with
4.7
(18,167 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
- Regression Analysis
- Classification Algorithms
- Logistic Regression
- Decision Tree Learning
- Predictive Modeling
- Feature Engineering
- Unsupervised Learning
- Applied Machine Learning
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Model Evaluation
- Python Programming
- Dimensionality Reduction
- Supervised Learning
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
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Learner reviews
4.7
18,167 reviews
- 5 stars
75.93%
- 4 stars
18.60%
- 3 stars
3.44%
- 2 stars
0.99%
- 1 star
1.01%
Showing 3 of 18167
RN
5
Reviewed on May 25, 2020
FG
5
Reviewed on Aug 28, 2019
Very informative course, showing mostly how to use many different Machine Learning techniques. Although mathematical details are not discussed much, the intuition of the methods are discussed.
RV
5
Reviewed on Jan 14, 2025
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.
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.
More questions
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