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: SAEED AGHABOZORGI
500,258 already enrolled
Included with 
4.7
(16,674 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
- Classification And Regression Tree (CART)
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Algorithms
- Statistical Machine Learning
- Unsupervised Learning
- Predictive Analytics
- Machine Learning Software
- Artificial Intelligence
- Analytics
- Applied Machine Learning
- Statistical Modeling
- Machine Learning
- Machine Learning Methods
- Data Science
- Scikit Learn (Machine Learning Library)
- Predictive Modeling
- Supervised Learning
- Data Analysis
- Computer Science
- Regression Analysis
Details to know
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Taught in English
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Learner reviews
4.7
16,674 reviews
- 5 stars
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3.36%
- 2 stars
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Showing 3 of 16674
RN
5
Reviewed on May 25, 2020
CA
4
Reviewed on Dec 31, 2019
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
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.
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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.