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Preprocessing for Machine Learning in Python

In this course you'll learn how to get your cleaned data ready for modeling.

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4 Hours20 Videos62 Exercises29,581 Learners4700 XPMachine Learning Scientist Track

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Course Description

This course covers the basics of how and when to perform data preprocessing. This essential step in any machine learning project is when you get your data ready for modeling. Between importing and cleaning your data and fitting your machine learning model is when preprocessing comes into play. You'll learn how to standardize your data so that it's in the right form for your model, create new features to best leverage the information in your dataset, and select the best features to improve your model fit. Finally, you'll have some practice preprocessing by getting a dataset on UFO sightings ready for modeling.

  1. 1

    Introduction to Data Preprocessing

    Free

    In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data.

    Play Chapter Now
    What is data preprocessing?
    50 xp
    Missing data - columns
    50 xp
    Missing data - rows
    100 xp
    Working with data types
    50 xp
    Exploring data types
    50 xp
    Converting a column type
    100 xp
    Class distribution
    50 xp
    Class imbalance
    50 xp
    Stratified sampling
    100 xp
  2. 2

    Standardizing Data

    This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance.

    Play Chapter Now
    Standardizing Data
    50 xp
    When to standardize
    50 xp
    Modeling without normalizing
    100 xp
    Log normalization
    50 xp
    Checking the variance
    50 xp
    Log normalization in Python
    100 xp
    Scaling data for feature comparison
    50 xp
    Scaling data - investigating columns
    50 xp
    Scaling data - standardizing columns
    100 xp
    Standardized data and modeling
    50 xp
    KNN on non-scaled data
    100 xp
    KNN on scaled data
    100 xp
  3. 3

    Feature Engineering

    In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features.

    Play Chapter Now
    Feature engineering
    50 xp
    Feature engineering knowledge test
    50 xp
    Identifying areas for feature engineering
    50 xp
    Encoding categorical variables
    50 xp
    Encoding categorical variables - binary
    100 xp
    Encoding categorical variables - one-hot
    100 xp
    Engineering numerical features
    50 xp
    Engineering numerical features - taking an average
    100 xp
    Engineering numerical features - datetime
    100 xp
    Text classification
    50 xp
    Engineering features from strings - extraction
    100 xp
    Engineering features from strings - tf/idf
    100 xp
    Text classification using tf/idf vectors
    100 xp
  4. 4

    Selecting features for modeling

    This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA).

    Play Chapter Now
    Feature selection
    50 xp
    When to use feature selection
    50 xp
    Identifying areas for feature selection
    50 xp
    Removing redundant features
    50 xp
    Selecting relevant features
    100 xp
    Checking for correlated features
    100 xp
    Selecting features using text vectors
    50 xp
    Exploring text vectors, part 1
    100 xp
    Exploring text vectors, part 2
    100 xp
    Training Naive Bayes with feature selection
    100 xp
    Dimensionality reduction
    50 xp
    Using PCA
    100 xp
    Training a model with PCA
    100 xp
  5. 5

    Putting it all together

    Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings.

    Play Chapter Now
    UFOs and preprocessing
    50 xp
    Checking column types
    100 xp
    Dropping missing data
    100 xp
    Categorical variables and standardization
    50 xp
    Extracting numbers from strings
    100 xp
    Identifying features for standardization
    100 xp
    Engineering new features
    50 xp
    Encoding categorical variables
    100 xp
    Features from dates
    100 xp
    Text vectorization
    100 xp
    Feature selection and modeling
    50 xp
    Selecting the ideal dataset
    100 xp
    Modeling the UFO dataset, part 1
    100 xp
    Modeling the UFO dataset, part 2
    100 xp
    Congratulations!
    50 xp

In the following tracks

Machine Learning Scientist

Datasets

Hiking dataWine dataUFO sightings dataVolunteering data

Collaborators

nicksolomonNick SolomonkarawooKara Woo

Prerequisites

Cleaning Data in PythonSupervised Learning with scikit-learn
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DataCamp Content Creator

Course Instructor

DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
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