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Clustering and Classification with Machine Learning in Python
Clustering and Classification with Machine Learning in Python

Clustering and Classification with Machine Learning in Python: Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

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Profile Icon Minerva Singh
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Video Dec 2019 5hrs 50mins 1st Edition
Video
zł39.99 zł730.99
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Free Trial
Arrow left icon
Profile Icon Minerva Singh
Arrow right icon
Free Trial
Video Dec 2019 5hrs 50mins 1st Edition
Video
zł39.99 zł730.99
Subscription
Free Trial
Video
zł39.99 zł730.99
Subscription
Free Trial

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Key benefits

  • Explore the most important Python data science concepts and packages, including Pandas
  • Master the Anaconda framework and use it to implement clustering and classification models on your data
  • Get to grips with data science fundamentals and understand which models should be used when

Description

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning. The course consists of 7 sections that will help you master Python machine learning. You’ll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you’ll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You’ll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You’ll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you’ll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you’ll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification. By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python. All code and supporting files for this course are available at https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.

Who is this book for?

If you want to build data science applications in the Python environment, this book is for you. You’ll also find this book helpful if you want to learn how to implement unsupervised learning on real data using Python, or if you’re a student looking to get started with artificial neural networks and deep learning.

What you will learn

  • Harness the power of Anaconda/iPython for practical data science
  • Read data into the Python environment from different sources
  • Carry out basic data preprocessing and wrangling in Python
  • Implement unsupervised/clustering techniques such as k-means clustering
  • Get to grips with dimensionality reduction techniques and feature selection
  • Implement supervised learning/classification techniques such as random forests
  • Explore neural network- and deep learning-based classification

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 30, 2019
Length: 5hrs 50mins
Edition : 1st
Language : English
ISBN-13 : 9781839213632
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Product Details

Publication date : Dec 30, 2019
Length: 5hrs 50mins
Edition : 1st
Language : English
ISBN-13 : 9781839213632
Category :
Languages :
Concepts :
Tools :

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Table of Contents

7 Chapters
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools Chevron down icon Chevron up icon
Read in Data from Different Sources with Pandas Chevron down icon Chevron up icon
Data Cleaning & Munging Chevron down icon Chevron up icon
Unsupervised Learning in Python Chevron down icon Chevron up icon
Dimension Reduction & Feature Selection for Machine Learning Chevron down icon Chevron up icon
Supervised Learning: Classification Chevron down icon Chevron up icon
Neural Networks and Deep Learning Based Classification Techniques Chevron down icon Chevron up icon
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