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Python Machine Learning: Ultimate Hands-On Beginner's Guide to Machine Learning in Python
Complete beginner’s guide to Machine Learning in Python
Machine learning has been a disruptive force in the world of software, and today it is being driven even further with deep learning
Want to get up to speed fast?
Completely up to date, this guide to Python Machine Learning includes a detailed treatment of the popular TensorFlow deep learning library, scikit-learn, and much more. By reading this book, you will be better prepared to meet the challenges and opportunities of data analysis that exist in the world today, and tomorrow.
Here is a preview of what you will learn in this guide:
What is Machine Learning?
Machine Learning Tasks
Supervised vs. Unsupervised Learning
Machine Learning Applications
Classification
Clustering
Density Estimation
Dimensionality Reduction
Regression Analysis
Machine Learning in Python
Scikit – learn
TensorFlow
What Version of Python to Use?
Installing the relevant libraries (If not using Anaconda)
Introductory Machine Learning in Python: Iris Flowers
Importing the Requisite Libraries and data
Summarizing the Data Set
Data Visualization
Creating Data Models
Making Predictions
Common Data Models Used in Machine Learning
Simple Linear Algorithms
Nonlinear Algorithms
What is a Neural Network?
Sample Neural Network Code
And so much more!
If you aren’t a tech-savvy person or have no programming or machine learning experience, have no fear! With this guide in your hands that will not be a barrier for you any longer. Understand Machine Learning in Python quickly and easily when you grab this guide now!
Machine learning has been a disruptive force in the world of software, and today it is being driven even further with deep learning
Want to get up to speed fast?
Completely up to date, this guide to Python Machine Learning includes a detailed treatment of the popular TensorFlow deep learning library, scikit-learn, and much more. By reading this book, you will be better prepared to meet the challenges and opportunities of data analysis that exist in the world today, and tomorrow.
Here is a preview of what you will learn in this guide:
What is Machine Learning?
Machine Learning Tasks
Supervised vs. Unsupervised Learning
Machine Learning Applications
Classification
Clustering
Density Estimation
Dimensionality Reduction
Regression Analysis
Machine Learning in Python
Scikit – learn
TensorFlow
What Version of Python to Use?
Installing the relevant libraries (If not using Anaconda)
Introductory Machine Learning in Python: Iris Flowers
Importing the Requisite Libraries and data
Summarizing the Data Set
Data Visualization
Creating Data Models
Making Predictions
Common Data Models Used in Machine Learning
Simple Linear Algorithms
Nonlinear Algorithms
What is a Neural Network?
Sample Neural Network Code
And so much more!
If you aren’t a tech-savvy person or have no programming or machine learning experience, have no fear! With this guide in your hands that will not be a barrier for you any longer. Understand Machine Learning in Python quickly and easily when you grab this guide now!
90 pages, Paperback
Published August 25, 2018
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