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Kursus

Rekayasa Fitur untuk Machine Learning di Python

MenengahTingkat Keterampilan

Diperbarui 02/2023

Buat fitur baru untuk meningkatkan kinerja model Machine Learning Anda.

Mulai Kursus Gratis

PythonMachine Learning

4 jam

16 videos

53 Latihan

4,350 XP

38,776

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Deskripsi Kursus

Setiap hari Anda mendengar terobosan luar biasa tentang bagaimana penerapan terbaru Machine Learning mengubah dunia. Sering kali pemberitaan tersebut mengabaikan fakta bahwa diperlukan banyak proses pembersihan data dan rekayasa fitur sebelum model canggih apa pun dapat digunakan. Dalam kursus ini, Anda akan mempelajari cara melakukannya. Anda akan bekerja dengan survei Stack Overflow Developers dan pidato pelantikan presiden AS sejarah, untuk memahami cara terbaik melakukan praproses dan merekayasa fitur dari data kategorikal, kontinu, dan tidak terstruktur. Kursus ini akan memberi Anda pengalaman langsung tentang cara menyiapkan data apa pun untuk model Machine Learning Anda sendiri.

Persyaratan

Supervised Learning with scikit-learn

1

Creating Features

In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

Why generate features?

Getting to know your data

Selecting specific data types

Dealing with categorical features

One-hot encoding and dummy variables

Dealing with uncommon categories

Numeric variables

Binarizing columns

Binning values

2

Dealing with Messy Data

This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

Why do missing values exist?

How sparse is my data?

Finding the missing values

Dealing with missing values (I)

Listwise deletion

Replacing missing values with constants

Dealing with missing values (II)

Filling continuous missing values

Imputing values in predictive models

Dealing with other data issues

Dealing with stray characters (I)

Dealing with stray characters (II)

Method chaining

3

Conforming to Statistical Assumptions

In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

Data distributions

What does your data look like? (I)

What does your data look like? (II)

When don't you have to transform your data?

Scaling and transformations

Normalization

Standardization

Log transformation

When can you use normalization?

Removing outliers

Percentage based outlier removal

Statistical outlier removal

Scaling and transforming new data

Train and testing transformations (I)

Train and testing transformations (II)

4

Dealing with Text Data

Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Encoding text

Cleaning up your text

High level text features

Word counts

Counting words (I)

Counting words (II)

Limiting your features

Text to DataFrame

Term frequency-inverse document frequency

Inspecting Tf-idf values

Transforming unseen data

Using longer n-grams

Finding the most common words

Rekayasa Fitur untuk Machine Learning di Python

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