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Active Machine Learning with Python

You're reading from   Active Machine Learning with Python Refine and elevate data quality over quantity with active learning

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835464946
Length 176 pages
Edition 1st Edition
Languages
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Author (1):
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Margaux Masson-Forsythe Margaux Masson-Forsythe
Author Profile Icon Margaux Masson-Forsythe
Margaux Masson-Forsythe
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Fundamentals of Active Machine Learning
2. Chapter 1: Introducing Active Machine Learning FREE CHAPTER 3. Chapter 2: Designing Query Strategy Frameworks 4. Chapter 3: Managing the Human in the Loop 5. Part 2: Active Machine Learning in Practice
6. Chapter 4: Applying Active Learning to Computer Vision 7. Chapter 5: Leveraging Active Learning for Big Data 8. Part 3: Applying Active Machine Learning to Real-World Projects
9. Chapter 6: Evaluating and Enhancing Efficiency 10. Chapter 7: Utilizing Tools and Packages for Active ML 11. Index 12. Other Books You May Enjoy

Technical requirements

For the exercises in this chapter, you will need to install these packages:

pip install scikit-learn
pip install modAL-python

And you will need the following imports:

from sklearn.cluster import KMeans
from sklearn.linear_model import LogisticRegression
from sklearn.utils import shuffle
import numpy as np
import random
from modAL.models import ActiveLearner, Committee
from sklearn.ensemble import RandomForestClassifier
from modAL.uncertainty import uncertainty_sampling
import os
from PIL import Image
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from modAL.disagreement import vote_entropy_sampling
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