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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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Machine Learning Algorithms & Markov Models Supervised And Unsupervised Learning with Python & Data Science 2 Manuscripts in 1 Book

/>Book 1 Machine Learning You will learn the fundamentals of machine learning from algorithms, python, supervised and;

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Hands-On Unsupervised Learning with Python

the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python;

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

art of data-driven problem-solving with hands-on examples Foster your theoretical and practical knowledge of supervised and unsupervised;

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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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Mastering Machine Learning Algorithms

with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi;

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Python Machine Learning Cookbook

to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common;

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Markov Models Supervised and Unsupervised Machine Learning

Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this;

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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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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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Machine Learning with PySpark

supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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Python Machine Learning Cookbook

market segmentation using unsupervised learning * Explore data visualization techniques to interact with your data in diverse ways * Find out;

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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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Data Science Solutions with Python

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer;

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Hands-On Data Science and Python Machine Learning

techniques of data analysis * Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods * Learn how;

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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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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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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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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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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Graphical Models for Machine Learning and Digital Communication

learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he;

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Pro Machine Learning Algorithms

implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic;

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Hands-on Scikit-Learn for Machine Learning Applications

book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised;

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