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
529,241 already enrolled
Included with 
4.7
(17,141 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
- Scikit Learn (Machine Learning Library)
- Jupyter
- Machine Learning Algorithms
- Statistical Machine Learning
- Dimensionality Reduction
- Unsupervised Learning
- Predictive Modeling
- Regression Analysis
- Supervised Learning
- Classification And Regression Tree (CART)
- Feature Engineering
- Statistical Modeling
- Applied Machine Learning
- Python Programming
- Random Forest Algorithm
- Matplotlib
- Data Manipulation
- Machine Learning
Details to know
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Taught in English
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Learner reviews
4.7
17,141 reviews
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76%
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- 2 stars
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Showing 3 of 17141
FO
5
Reviewed on Oct 9, 2020
IK
5
Reviewed on Dec 14, 2022
Thank you Coursera & IBM for offering such a wonderful career-oriented course. Thank you very much Dr SAEED AGHABOZORGI and Dr Joseph Santarcangelo for providing the amazing learning Journey.
AJ
5
Reviewed on Jul 9, 2019
This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.
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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.
More questions
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