ML Introduction with scikit-learn
Course
3106 Learners Already enrolled- Gain foundational insights into Machine Learning concepts.
- Explore data types and preprocessing techniques in Scikit-learn.
- Understand Machine Learning workflows including training sets and models.
- Grasp data scaling methods: StandardScaler, MinMaxScaler, MaxAbsScaler.
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There are 4 modules in this course
Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project. This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.- Scikit-learn ConceptsPreview
- Getting Familiar with DatasetPreview
- Dealing with Missing ValuesPreview
- Challenge: Imputing Missing ValuesPreview
- OrdinalEncoderPreview
- One-Hot EncoderPreview
- LabelEncoderPreview
- Challenge: Encoding Categorical VariablesPreview
- Why Scale the Data?Preview
- StandardScaler, MinMaxScaler, MaxAbsScalerPreview
- Challenge: Scaling the FeaturesPreview
- ModelsPreview
- KNeighborsClassifierPreview
- Evaluating the ModelPreview
- Cross-ValidationPreview
- Challenge: Evaluating the Model with Cross-ValidationPreview
- GridSearchCVPreview
- The Flaw of GridSearchCVPreview
- Challenge: Tuning Hyperparameters with RandomizedSearchCVPreview
- Modeling SummaryPreview
- Challenge: Putting It All TogetherPreview
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Duy Tran
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Reviewed on Apr 17, 2025
Theophilus Makiwane
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Reviewed on Mar 23, 2025
Material is easy to read and understand
Eoghan Gormley
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Reviewed on Mar 22, 2025
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