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Supervised Learning with scikit-learn

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

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4 Hours15 Videos49 Exercises13,843 Learners4050 XPData Scientist TrackMachine Learning Fundamentals TrackMachine Learning Scientist Track

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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.

  1. 1

    Classification

    Free

    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.

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    Machine learning with scikit-learn
    50 xp
    Binary classification
    50 xp
    The supervised learning workflow
    100 xp
    The classification challenge
    50 xp
    k-Nearest Neighbors: Fit
    100 xp
    k-Nearest Neighbors: Predict
    100 xp
    Measuring model performance
    50 xp
    Train/test split + computing accuracy
    100 xp
    Overfitting and underfitting
    100 xp
    Visualizing model complexity
    100 xp
  2. 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.

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  3. 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.

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In the following tracks

Data Scientist Machine Learning FundamentalsMachine Learning Scientist

Collaborators

james-datacamp
James Chapman
amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson
izzyweber-9bc35945-95bd-423b-833e-40780c76586f
Izzy Weber
George Boorman Headshot

George Boorman

Core Curriculum Manager, DataCamp

George is a Core Curriculum Manager at DataCamp. He has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about health technologies and all things data science.
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