After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Grow your machine learning skills with scikit-learn and discover how to use this popular Python library to train models using labeled data. In this course, you'll learn how to make powerful predictions, such as whether a customer is will churn from your business, whether an individual has diabetes, and even how to tell classify the genre of a song. Using real-world datasets, you'll find out how to build predictive models, tune their parameters, and determine how well they will perform with unseen data.
The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Assess model generalization using train-test splits, k-fold cross-validation, and hyperparameter tuning with GridSearchCV or RandomizedSearchCV
- Differentiate key evaluation metrics for supervised models, including accuracy, precision, recall, F1, ROC-AUC, R-squared, MSE, and RMSE
- Evaluate model complexity and its impact on overfitting or underfitting by adjusting parameters such as k in KNN and alpha in regularized regression.
- Identify supervised learning problem types and select appropriate scikit-learn algorithms for classification and regression
- Recognize essential preprocessing techniques—dummy encoding, imputation, scaling, and pipeline construction—required for scikit-learn workflows
Prerequisites
Curriculum
Course outline
1
Classification
In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.
- Machine learning with scikit-learn50 XP
- Binary classification50 XP
- The supervised learning workflow100 XP
- The classification challenge50 XP
- k-Nearest Neighbors: Fit100 XP
- k-Nearest Neighbors: Predict100 XP
- Measuring model performance50 XP
- Train/test split + computing accuracy100 XP
- Overfitting and underfitting100 XP
- Visualizing model complexity100 XP
2
Regression
In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.
3
Fine-Tuning Your Model
Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.
4
Preprocessing and Pipelines
Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!
Supervised Learning with scikit-learn
Course
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