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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 with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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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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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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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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Machine Learning Fundamentals

Understand the difference between supervised and unsupervised models Learn the usage of machine learning through real-world examples Book;

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Hands-On Unsupervised Learning with Python

. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best;

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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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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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Mastering Machine Learning Algorithms

machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised;

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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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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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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 Machine Learning with scikit-learn and Scientific Python Toolkits

to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning;

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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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Econometrics and Data Science

author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning;

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Applied Machine Learning for Smart Data Analysis

unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections;

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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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HandsOn Unsupervised Learning Using Python

different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end;

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Supervised Machine Learning with Python

Teach your machine to think for itself! Key Features Delve into supervised learning and grasp how a machine learns from data Implement;

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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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Machine Learning with Scala Quick Start Guide

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key Features Construct and deploy machine;

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Natural Language Processing

learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and;

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An Introduction to Machine Learning

that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov;

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Introduction to Semi-Supervised Learning

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the;

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Introduction to Machine Learning with R

learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear;

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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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