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Supervised Learning with scikit-learn

4.4+
120 reviews
Intermediate

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 Exercises
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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.
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In the following Tracks

Certification Available

Associate Data Scientist in Python

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Machine Learning Fundamentals with Python

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Machine Learning Scientist with Python

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  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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GroupTraining 2 or more people?

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In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Machine Learning Fundamentals with Python

Go To Track

Machine Learning Scientist with Python

Go To Track

In other tracks

Supervised Machine Learning in Python

Collaborators

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James Chapman
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Amy Peterson
Collaborator's avatar
Izzy Weber
George Boorman HeadshotGeorge Boorman

Curriculum Manager, DataCamp

George is a Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.
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Don’t just take our word for it

*4.4
from 120 reviews
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  • Caroline B.
    about 2 months

    Amazing!!

  • Rahul K.
    2 months

    It is often hard to know where to start when learning ML in python. This course covers sufficent breadth and depth to start the journey, introduce core concepts, and outline the fundamentals for building a supervised ML model. Would highly recommend with anyone with python experience but no ML experience.

  • OLIVER M.
    4 months

    Excelente curso

  • George B.
    5 months

    Hey, what a professor! Please add more courses from him.

  • Carlos B.
    6 months

    Excelente curso. Brinda una visión general del tema con ejemplos muy claros.

"Amazing!!"

Caroline B.

"It is often hard to know where to start when learning ML in python. This course covers sufficent breadth and depth to start the journey, introduce core concepts, and outline the fundamentals for building a supervised ML model. Would highly recommend with anyone with python experience but no ML experience."

Rahul K.

"Excelente curso"

OLIVER M.

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