Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python
Rudolph Russell

This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a practical way, and we'll implement many machine-learning algorithms using the Scikit-learn library in the Python programming language. In the first chapter, you'll learn the most important concepts of machine learning, and, in the next chapter, you'll work mainly with the classification. In the last chapter you'll learn how to train your model. I assume that you've knowledge of the basics of programming

#python #eng #MachineLearning

Machine Learning with TensorFlow
Nishant Shukla with Kenneth Fricklas

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

#python #eng #MachineLearning

Python for Programmers: with Big Data and Artificial Intelligence Case Studies
Paul Deitel, Dr. Harvey Deitel

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1—5 and a few key parts of Chapters 6—7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11—16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter for sentiment analysis, cognitive computing with IBM Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and

#python #eng #MachineLearning #data

Building Chatbots with Python: Using Natural Language Processing and Machine Learning
Sumit Raj

#python #eng #MachineLearning

Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python
Jason Brownlee

Some classical methods used in the field of linear algebra,such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. This book helps machine learning practitioners, get on top of linear algebra, fast.

#python #eng #MachineLearning

Python for Probability, Statistics, and Machine Learning
José Unpingco

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads.
#python #eng #MachineLearning

Deep Learning with TensorFlow
Giancarlo Zaccone, Md. Rezaul Karim

This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.
#python #eng #MachineLearning

Hands on Machine Learning with Scikit Learn Keras and TensorFlow
Aurélien Géron

Explore the machine learning landscape, particularly neural nets
Use Scikit-Learn to track an example machine-learning project end-to-end
Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
Use the TensorFlow library to build and train neural nets
Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
Learn techniques for training and scaling deep neural nets
#python #eng #MachineLearning

✌️Коллеги! Для удобства поиска информации в ленте, под каждым постом есть тег. Все теги я оставляю в закрепе, список тегов постоянно пополняется. 👇
Библиотека:
#Angular #english #rus #Deep_Learning #python #eng #graphics #Image #LearningScrapy #beginners #coding #MachineLearning #data #JavaDeveloper #projects #BioinformaticsAlgorithms #cryptography #forkids #epub #cheatsheet #django #Bhasin #science #spacagna #network #flask #TypeScript #самоучитель #справочник

Все остальное:
#programming #KivyMD #mobile #задачи #dictionary #обучение

📚Книга: "Машинное обучение: основы, алгоритмы и практика применения."
#python #ru #MachineLearning
Автор: Jeremy Watt
Год выхода: 2022
Кол. страниц: 642


Представлены фундаментальные знания и практические инструменты в области машинного обучения, в том числе более 100 углубленных упражнений на языке Python.

Дано введение в машинное обучение и математическую оптимизацию, включая методы первого и второго порядков, градиентного спуска и Ньютона. Приведено полное описание обучения с учителем, включая линейную регрессию, двухклассовую и многоклассовую классификацию, а также обучение без учителя и фундаментальные методы генерации признаков Дано введение в нелинейное обучение с учителем и без. Обсуждается тема автоматизированного отбора подходящих нелинейных моделей, включая перекрестную валидацию, бустирование, регуляризацию и ансамблирование. Рассмотрены фиксированно-контурные ядра, нейронные сети, деревья и другие универсальные аппроксиматоры.

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