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Machine Learning for Kids (Tinker Toddlers)

, toddlers, and preschoolers. Look around you. What machines do you see? From the award-winning Tinker Toddlers series, Machine Learning for Kids;

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Neural Networks for Kids (Tinker Toddlers)

, toddlers, and preschoolers. Did you know machines can learn, just like you? From the award-winning Tinker Toddlers series, Neural Networks for Kids;

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Artificial Intelligence for Kids (Tinker Toddlers)

curiosity (and might stimulate your senses too)! Told with 2 levels of learning: Level 1: Black text for baby basics Level 2: Purple text for early;

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Solar System for Kids (Tinker Toddlers)

, adventure, and curiosity (and might stimulate your senses too)! Told with 2 levels of learning: Level 1: Black text for baby basics Level 2: Purple;

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Solar System for Kids (Tinker Toddlers)

, adventure, and curiosity (and might stimulate your senses too)! Told with 2 levels of learning: Level 1: Black text for baby basics Level 2: Purple;

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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

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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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

> Features various supervised machine learning based regression models Offers global case studies for turbine wind farm layouts

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TensorFlow 2.0 Quick Start Guide

your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced;

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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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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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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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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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Applied Supervised Learning with Python

Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some;

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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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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 Supervised Learning with R

DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised;

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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 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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Einde inhoud

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