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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 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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Supervised Machine Learning for Kids (Tinker Toddlers)

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Supervised Machine Learning for Kids (Tinker Toddlers)

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind;

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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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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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Applied Supervised Learning with Python

learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into;

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Mastering Machine Learning Algorithms

data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement;

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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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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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Medical Image Computing and Computer Assisted Intervention - MICCAI 2021

papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised;

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Supervised Machine Learning

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Applied Supervised Learning with R

Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object;

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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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Supervised Machine Learning for Text Analysis in R

workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data;

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Supervised Machine Learning for Text Analysis in R

workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data;

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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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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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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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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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Semi-Supervised Learning

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;

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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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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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