Statistics for machine learning: build supervised, unsupervised, and reinforcement learning models using both Python and R

Statistics for Machine Learning: Techniq

📁 Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

✍ Pratap Dangeti 📂 Library 📅 2017 🏛 Packt Publishing 🌐 English

<h4>Key Features</h4><ul><li>Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.</li><li>Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.</li><li>Master the statistical aspect of

Statistics for Machine Learning: Techniq

📁 Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

✍ Pratap Dangeti 📂 Library 📅 2017 🏛 Packt Publishing 🌐 English

<h4>Key Features</h4><ul><li>Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.</li><li>Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.</li><li>Master the statistical aspect of

R Machine Learning Projects: Implement s

📁 R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

✍ Dr. Sunil Kumar Chinnamgari 📂 Library 📅 2019 🏛 Packt Publishing 🌐 English

<p><b>Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more</b></p> <h4>Key Features</h4> <ul><li>Master machine learning, deep learning, and predictive modeling concepts in R 3.5 </li> <li>Build intelligent end-to-end projects for finance,

R Machine Learning Projects: Implement s

📁 R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

✍ Dr. Sunil Kumar Chinnamgari 📂 Library 📅 2019 🏛 Packt Publishing 🌐 English

<p><b>Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more</b></p> <h4>Key Features</h4> <ul><li>Master machine learning, deep learning, and predictive modeling concepts in R 3.5 </li> <li>Build intelligent end-to-end projects for finance,

Machine Learning Foundations: Supervised

📁 Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning

✍ Taeho Jo 📂 Library 📅 2021 🏛 Springer 🌐 English

<p>This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental mat

Deep Learning with TensorFlow and Keras:

📁 Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

✍ Amita Kapoor, Antonio Gulli, Sujit Pal 📂 Library 📅 2022 🏛 Packt Publishing 🌐 English

<p><span>Build cutting edge machine and deep learning systems for the lab, production, and mobile devices</span></p><h4><span>Key Features</span></h4><ul><li><span><span>Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples</span></sp

Statistics for machine learning: build supervised, unsupervised, and reinforcement learning models using both Python and R

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