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Machine Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

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4 Hours17 Videos54 Exercises316,850 Learners4200 XP

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Course Description

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.
  1. 1

    Classification

    Free

    In this chapter, you will be introduced to classification problems and learn how to solve them using supervised learning techniques. And you’ll apply what you learn to a political dataset, where you classify the party affiliation of United States congressmen based on their voting records.

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    Supervised learning
    50 xp
    Which of these is a classification problem?
    50 xp
    Exploratory data analysis
    50 xp
    Numerical EDA
    50 xp
    Visual EDA
    50 xp
    The classification challenge
    50 xp
    k-Nearest Neighbors: Fit
    100 xp
    k-Nearest Neighbors: Predict
    100 xp
    Measuring model performance
    50 xp
    The digits recognition dataset
    100 xp
    Train/Test Split + Fit/Predict/Accuracy
    100 xp
    Overfitting and underfitting
    100 xp
  2. 2

    Regression

    In the previous chapter, you used image and political datasets to predict binary and multiclass outcomes. But what if your problem requires a continuous outcome? Regression is best suited to solving such problems. You will learn about fundamental concepts in regression and apply them to predict the life expectancy in a given country using Gapminder data.

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    Introduction to regression
    50 xp
    Which of the following is a regression problem?
    50 xp
    Importing data for supervised learning
    100 xp
    Exploring the Gapminder data
    50 xp
    The basics of linear regression
    50 xp
    Fit & predict for regression
    100 xp
    Train/test split for regression
    100 xp
    Cross-validation
    50 xp
    5-fold cross-validation
    100 xp
    K-Fold CV comparison
    100 xp
    Regularized regression
    50 xp
    Regularization I: Lasso
    100 xp
    Regularization II: Ridge
    100 xp
  3. 3

    Fine-tuning your model

    Having trained your model, your next task is to evaluate its performance. In this chapter, you will learn about some of the other metrics available in scikit-learn that will allow you to assess your model's performance in a more nuanced manner. Next, learn to optimize your classification and regression models using hyperparameter tuning.

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    How good is your model?
    50 xp
    Metrics for classification
    100 xp
    Logistic regression and the ROC curve
    50 xp
    Building a logistic regression model
    100 xp
    Plotting an ROC curve
    100 xp
    Precision-recall Curve
    50 xp
    Area under the ROC curve
    50 xp
    AUC computation
    100 xp
    Hyperparameter tuning
    50 xp
    Hyperparameter tuning with GridSearchCV
    100 xp
    Hyperparameter tuning with RandomizedSearchCV
    100 xp
    Hold-out set for final evaluation
    50 xp
    Hold-out set reasoning
    50 xp
    Hold-out set in practice I: Classification
    100 xp
    Hold-out set in practice II: Regression
    100 xp
  4. 4

    Preprocessing and pipelines

    This chapter introduces pipelines, and how scikit-learn allows for transformers and estimators to be chained together and used as a single unit. Preprocessing techniques will be introduced as a way to enhance model performance, and pipelines will tie together concepts from previous chapters.

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    Preprocessing data
    50 xp
    Exploring categorical features
    100 xp
    Creating dummy variables
    100 xp
    Regression with categorical features
    100 xp
    Handling missing data
    50 xp
    Dropping missing data
    100 xp
    Imputing missing data in a ML Pipeline I
    100 xp
    Imputing missing data in a ML Pipeline II
    100 xp
    Centering and scaling
    50 xp
    Centering and scaling your data
    100 xp
    Centering and scaling in a pipeline
    100 xp
    Bringing it all together I: Pipeline for classification
    100 xp
    Bringing it all together II: Pipeline for regression
    100 xp
    Final thoughts
    50 xp

Datasets

Automobile miles per gallonBoston housingDiabetesGapminderUS Congressional Voting Records (1984)White wine qualityRed wine quality

Collaborators

Yashas Roy

Prerequisites

Statistical Thinking in Python (Part 1)
Hugo Bowne-Anderson Headshot

Hugo Bowne-Anderson

Data Scientist at DataCamp

Hugo is a data scientist, educator, writer and podcaster at DataCamp. His main interests are promoting data & AI literacy, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. If you want to know what he likes to talk about, definitely check out DataFramed, the DataCamp podcast, which he hosts and produces: https://www.datacamp.com/community/podcast
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