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Applied Supervised Learning with Python

input data-has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply;

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Python Machine Learning

Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;

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Machine Learning with scikit-learn Quick Start Guide

DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning;

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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;

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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;

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Hands-on Machine Learning with Python

learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;

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Supervised Machine Learning with Python

learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming-and some fundamental knowledge;

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Mastering Machine Learning Algorithms

learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy;

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Hands-on Machine Learning with Python

learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the;

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TensorFlow 2.0 Quick Start Guide

is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest;

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Python Machine Learning Cookbook

deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.;

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The The Supervised Learning Workshop

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key Features;

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Welding and Cutting Case Studies with Supervised Machine Learning

learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also;

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Welding and Cutting Case Studies with Supervised Machine Learning

learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also;

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Machine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image;

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Hands-On Unsupervised Learning with Python

Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning;

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Machine Learning Fundamentals

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and;

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Machine Learning and Data Science Blueprints for Finance From Building Trading Strategies to RoboAdvisors Using Python

over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for;

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Applied Machine Learning with Python

such as XGBoost (2016), Shap (2017) and CatBoost (2018), which are considered novel and cutting edge models for dealing with supervised;

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Data Science Solutions with Python

learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Beginning Anomaly Detection Using Python-Based Deep Learning

and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly;

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Hands-On Data Science and Python Machine Learning

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale;

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HandsOn Unsupervised Learning Using Python

artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised;

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