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upload/trantor/en/Fenner, Mark E/Machine Learning with Python for Everyone ú 1st Edition.epub

Machine Learning with Python for Everyone, First Edition Mark E. Fenner Addison-Wesley Professional, 2019

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

English [en] · EPUB · 16.3MB · 2019 · 📗 Book (unknown) · 🚀/upload/zlib · Save

base score: 11065.0, final score: 167555.62

nexusstc/Machine Learning with Python for Everyone, First Edition/e4b87af300b79ad9880ab926e27a624c.pdf

Machine Learning with Python for Everyone, First Edition Mark Fenner Addison-Wesley Professional, 2019

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with PythonMachine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.Understand machine learning algorithms, models, and core machine learning conceptsClassify examples with classifiers, and quantify examples with regressorsRealistically assess performance of machine learning systemsUse feature engineering to smooth rough data into useful formsChain multiple components into one system and tune its performanceApply machine learning techniques to images and textConnect the core concepts to neural networks and graphical modelsLeverage the Python scikit-learn library and other powerful toolsRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

English [en] · PDF · 16.2MB · 2019 · 📗 Book (unknown) · 🚀/nexusstc/zlib · Save

base score: 11065.0, final score: 167552.52

lgli/Fenner M. Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) (AW, 2019)(ISBN 9780134845623)(F)(O)(588s)_CsAi_.pdf

Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) Mark E Fenner Addison-Wesley Professional, 1, PS, 2019

English [en] · PDF · 6.2MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save

base score: 11065.0, final score: 167545.02

lgli/r:\!fiction\0day\1\Machine Learning with Python for Everyone - Mark E. Fenner (Addison-Wesley Professional;Addison-Wesley Data & Analytics Series;2019;9780134845623;eng).epub

Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) Mark E. Fenner Pearson Education Limited (US titles);Addison Wesley Professional, Addison-Wesley Data & Analytics Series, 1st edition, 2019

**The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python**__**Machine Learning with Python for Everyone**__will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools__Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.__

English [en] · EPUB · 54.0MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 167543.72

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Machine Learning with Python for Everyone Mark E. Fenner

English [en] · PDF · 9.4MB · 📘 Book (non-fiction) · 🚀/zlib · Save

base score: 11058.0, final score: 167533.64

nexusstc/Machine Learning with Python for Everyone/466dc624617160bb80581a33458c3bb4.epub

English [en] · EPUB · 53.8MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 167529.05

lgli/P:\kat_magz\Assorted Books Collection - 27 November 2020 Part-3/Machine Learning With Python For Everyone.pdf

English [en] · PDF · 9.4MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 167527.7

nexusstc/Machine Learning with Python for Everyone/6a69fdd7a22b29b02f6581648db92eed.epub

English [en] · EPUB · 16.3MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 167527.66

Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) Mark E Fenner Addison-Wesley Professional, Addison-Wesley Data & Analytics Series), 1, 2019

English [en] · PDF · 6.5MB · 2019 · 📗 Book (unknown) · 🚀/zlib · Save

base score: 11068.0, final score: 167507.72

34 partial matches

upload/newsarch_ebooks/2020/08/29/Python Machine Learning_ How to learn Machine Learning with Python. The Complete Guide to Understand Python Machine Learning for Beginners and Artificial Intelligence.epub

Python Machine Learning: How to learn Machine Learning with Python. The Complete Guide to Understand Python Machine Learning for Beginners and Artificial Intelligence Soranson, Oliver 2019

English [en] · EPUB · 0.9MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

base score: 11055.0, final score: 51.577682

nexusstc/Machine Learning with Python: Complete Step-by-Step Guide for Beginners to Learning Machine Learning Technology, Principles, Application and The Importance It Has Today/105724ed238951135d780a5d921295fe.epub

Machine Learning with Python: Complete Step-by-Step Guide for Beginners to Learning Machine Learning Technology, Principles, Application and The Importance It Has Today Park, David 2019

English [en] · EPUB · 2.4MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11060.0, final score: 50.625317

upload/wll/ENTER/1 ebook Collections/Z - More books, UNSORTED Ebooks/1 - More books/Python Machine Learning - The Ultimate Guide for Beginners to Machine Learning with Python.epub

Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science Moore, Richard; Moore, Richard 2019

English [en] · EPUB · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

base score: 11060.0, final score: 50.460815

nexusstc/Python for Beginners: Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python./0552c3cd173aa42cec279d091490c751.azw3

Python for Beginners: Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python. Alex Campbell

Python is one of the most powerful computer programming languages of all time, for several reasons we’ll discuss in the first section. The syntax is simple to learn and use and, compared to other programming languages, you often don’t need to write so much code. The sheer simplicity of the language helps programmers write more and develop programs that are more complex in less time. This guide provides all you need to master the basics of programming with Python . I have kept it deliberately simple – it is a quick-start guide, after all. I have provided plenty of coding examples to show you how the syntax works, too, along with a guide on installing Python on Windows, Mac, and Linux systems. I finish with some useful tips on helping you to code better. By the end of the guide, you will have a deeper understanding of the Python language, a stepping stone from which to take your learning further. Please note that we are using Python 3 in this guide, not Python 2, as many similar guides do. Are you ready to become a computer programmer? Let’s get started on this wonderful journey.

English [en] · AZW3 · 3.6MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11051.0, final score: 50.060963

upload/wll/ENTER/1 ebook Collections/Z - More books, UNSORTED Ebooks/1 - More books/Machine Learning with Python - A Step by Step Guide for Absolute Beginners to Program Artificial.epub

Machine Learning with Python: A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python. Neural Networks and Data Science from Pre-Processing to Deep Learning CODING, MARK 2020

English [en] · EPUB · 3.3MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

base score: 11060.0, final score: 49.86151

lgli/Python Programming Handbook For Machine Learning With Pytorch And Scikit-learn.epub

Python Programming Handbook For Machine Learning With Pytorch And Scikit-learn : A Complete Beginners Guide To Learning Essential Skills To Build Deep Learning Models With Python Mackay , Hazel Independently published, 2024

Unlock the Power of Machine Learning with Python! Welcome to the Python Programming Handbook for Machine Learning with PyTorch and Scikit-learn! Are you ready to dive into the world of machine learning and unlock its secrets? This comprehensive handbook is your ultimate guide to mastering machine learning with Python, using two of the most popular and powerful libraries: PyTorch and Scikit-learn. With this book, you'll learn how to - Build and train machine learning models with PyTorch and Scikit-learn - Load and preprocess data for machine learning - Implement supervised and unsupervised learning techniques - Use deep learning models for computer vision and natural language processing - Deploy and integrate machine learning models in real-world applications But that's not all! This handbook is packed with - Interactive examples and exercises to help you learn by doing - Real-world case studies and projects to apply your skills - Tips and tricks from experienced machine learning practitioners - A comprehensive introduction to machine learning fundamentals Whether you're a beginner or an experienced developer, this handbook is your go-to resource for mastering machine learning with Python.

English [en] · EPUB · 0.7MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save

base score: 11055.0, final score: 49.641296

upload/newsarch_ebooks_2025_10/2023/05/17/B09QYQC31S.epub

Python Machine Learning for Beginners: All You Need to Know about Machine Learning with Python Alex Campbell

Have you thought about a career in data science? It's where the money is right now, and it's only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it's the next logical step. Machine learning is a subsection of AI, or Artificial Intelligence, and computer science, using data and algorithms to imitate human thinking and learning. Through constant learning, machine learning gradually improves its accuracy, eventually providing the optimal results for the problem it has been assigned to. It is one of the most important parts of data science and, as big data continues to expand, so too will the need for machine learning and AI. Here's what you will learn in this quick guide to machine learning with Python for beginners: What machine learning is Why Python is the best computer programming language for machine learning The different types of machine learning How linear regression works The different types of classification How to use SVMs (Support Vector Machines) with Scikit-Learn How Decision Trees work with Classification How K-Nearest Neighbors works How to find patterns in data with unsupervised learning algorithms You will also find plenty of code examples to help you understand how everything works. If you are ready to take your programming further, scroll up, click Buy Now, and find out why machine learning is the next logical step.

English [en] · EPUB · 0.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

base score: 10051.0, final score: 49.440266

nexusstc/Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions/b99c0334b32249b9cf434f51e47238ea.pdf

MACHINE LEARNING FOR STREAMING DATA WITH PYTHON : rapidly build practical online machine learning... solutions using river and other top key frameworks Joos Korstanje Packt Publishing, Limited, 1st edition, Birmingham, copyright © 2022

Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features Work on streaming use cases that are not taught in most data science courses Gain experience with state-of-the-art tools for streaming data Mitigate various challenges while handling streaming data

English [en] · PDF · 7.3MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 49.329292

Python Machine Learning: How to learn Machine Learning with Python. The Complete Guide to Understand Python Machine Learning for Beginners and Artificial Intelligence Soranson, Oliver 2019

Introduction 5 Chapter 1 : Introduction ( A Small History of Machine Learning ) 7 Chapter 2 : The Concept of Machine Learning 17 Chapter 3 : Mathematical Notation , Basic Terminology , and Building Machine Learning Systems 26 Chapter 4 : Using Python for Machine Learning 36 Chapter 5 : Artificial Neural Networks 49 Chapter 6 : Machine Learning Classification 58 Chapter 7 : Machine Learning Training Model 66 Chapter 8 : Developing a Machine Learning Model with Python 70 Chapter 9 : Training Simple Machine Learning Algorithms for Classification 79 Chapter 10 : Building Good Training Sets 92 Conclusion 97

English [en] · PDF · 0.8MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/upload/zlib · Save

base score: 11058.0, final score: 49.228634

lgli/Moore, Richard [Moore, Richard] - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science (2019, ).lit

English [en] · LIT · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11048.0, final score: 48.970093

nexusstc/Introduction To Conformal Prediction With Python : A Short Guide For Quantifying Uncertainty Of Machine Learning Models/2a976e9cbe102232182905ad68dde250.pdf

Introduction To Conformal Prediction With Python : A Short Guide For Quantifying Uncertainty Of Machine Learning Models Christoph Molnar MUCBOOK, 1, 2023

Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. "This concise book is accessible, lucid, and full of helpful code snippets. It explains the mathematical ideas with clarity and provides the reader with practical examples that illustrate the essence of conformal prediction, a powerful idea for uncertainty quantification." – Junaid Butt, Research Software Engineer, IBM Research Summary A prerequisite for trust in machine learning is uncertainty quantification. Without it, an accurate prediction and a wild guess look the same. Yet many machine learning models come without uncertainty quantification. And while there are many approaches to uncertainty – from Bayesian posteriors to bootstrapping – we have no guarantees that these approaches will perform well on new data. At first glance conformal prediction seems like yet another contender. But conformal prediction can work in combination with any other uncertainty approach and has many advantages that make it stand out Guaranteed coverage: Prediction regions generated by conformal prediction come with coverage guarantees of the true outcome Easy to use: Conformal prediction approaches can be implemented from scratch with just a few lines of code Model-agnostic: Conformal prediction works with any machine learning model Distribution-free: Conformal prediction makes no distributional assumptions No retraining required: Conformal prediction can be used without retraining the model Broad application: conformal prediction works for classification, regression, time series forecasting, and many other tasks Sound good?

English [en] · PDF · 6.1MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

base score: 11065.0, final score: 48.931137

lgli/Richard Moore [Moore, Richard] - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science (2019, ).mobi

English [en] · MOBI · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.930363

English [en] · MOBI · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.857723

lgli/Park, Andrew - Python Machine Learning: A Complete Guide for Beginners on Machine Learning and Deep Learning with Python 4(2021, ).pdf

Python Machine Learning: A Complete Guide for Beginners on Machine Learning and Deep Learning with Python 4 4 Park, Andrew 4, 2021

Machine Learning is as much about programming as it is about probability and statistics. There are many statistical approaches that we will use in Machine Learning to help us arrive at optimal solutions from time to time. It is therefore important that you remind yourself about some of the necessary probability theories and how they affect outcomes in each scenario.In our studies of Machine Learning from the beginner books through an intermediary level to this point, one concept that stands out is that Machine Learning involves uncertainty. This is one of the differences between Machine Learning and programming. In programming, you write code that must be executed as it is written. The code derives a predetermined output based on the instructions given. However, in Machine Learning, this is not a luxury we enjoy.

English [en] · PDF · 1.4MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11066.0, final score: 48.857723

lgli/Python Programming Workbook For Machine Learning With Pytorch And Scikit-Learn.epub

Python Programming Workbook For Machine Learning With Pytorch And Scikit-Learn French , Adrian M. Independently Published, 2024

workbook!This practical guide equips you with the skills and knowledge to build effective machine learning models using popular libraries like PyTorch and scikit-learn. Through a series of hands-on exercises, you'll gain a deep understanding of essential concepts and techniques, while simultaneously developing your Python programming proficiency. Key Features Master the Fundamentals: Grasp the core principles of machine learning, including data preprocessing, model selection, evaluation metrics, and project life cycle management. Dive into PyTorch: Explore the power of PyTorch for building neural networks. Master tensors, autograd, and the core functionalities to design and train custom deep learning architectures. Harness the Power of scikit-learn: Leverage scikit-learn's extensive toolkit for traditional machine learning algorithms. Learn to implement logistic regression, gradient boosting techniques like XGBoost and LightGBM, and more. Data Wrangling Mastery: Discover effective data transformation techniques with NumPy and Pandas, the workhorses of data manipulation in Python. Learn feature engineering to prepare your data for optimal model performance. Visualization Powerhouse: Utilize Matplotlib to create informative visualizations that aid in data exploration, model evaluation, and clear communication of results. Project Development Workflow: Gain insights into a structured approach to machine learning project development. Learn to efficiently navigate the stages of problem definition, data acquisition, model selection, training, evaluation, and deployment. Advanced Techniques: Delve into advanced topics like convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequence modeling with PyTorch, and Long Short-Term Memory (LSTM) networks for handling long-term dependencies By the end of this workbook, you'll be able to Confidently build and train machine learning models using Python Implement a variety of traditional and deep learning algorithms with PyTorch and scikit-learn Preprocess and transform data effectively for optimal machine learning performance Create insightful data visualizations to better understand your models and findings Develop a systematic approach to machine learning project development Apply advanced techniques like CNNs, RNNs, and LSTMs to complex tasks Whether you're a beginner eager to enter the machine learning field or an experienced programmer looking to broaden your skillset, this workbook is your essential companion!

English [en] · EPUB · 0.5MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save

base score: 11055.0, final score: 48.837357

lgli/Moore, Richard - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science (2019, ).fb2

English [en] · FB2 · 2.3MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.835033

upload/newsarch_ebooks_2025_10/2021/05/20/extracted__B095D8GQSX.7z/MACHINE LEARNING WITH PYTHON_ Step by Step Guide to Build ARTIFICIAL INTELLIGENCE Systems using Python, Scikit-learn, for Machine Learning, Deep Learning & Data Science.epub

MACHINE LEARNING WITH PYTHON: Step by Step Guide to Build ARTIFICIAL INTELLIGENCE Systems using Python, Scikit-learn, for Machine Learning, Deep Learning & Data Science Erickson, Benjamin Drew

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base score: 10951.0, final score: 48.81771

English [en] · AZW3 · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.80746

lgli/Moore & Richard - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science.rtf

Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science Moore & Richard

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base score: 11036.0, final score: 48.80746

English [en] · MOBI · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.80014

lgli/Samuel Burns - Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) (2018, cj5).epub

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) Samuel Burns cj5, 2018

English [en] · EPUB · 0.6MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11055.0, final score: 48.76566

English [en] · EPUB · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11063.0, final score: 48.59728

lgli/Andrew Park - Machine Learning: 2 Books in 1: Python Machine Learning and Data Science. A Comprehensive Guide for Beginners to Master Deep Learning, Artificial Intelligence and Data Science with Python. (2020, ).epub

Machine Learning: 2 Books in 1: Python Machine Learning and Data Science. A Comprehensive Guide for Beginners to Master Deep Learning, Artificial Intelligence and Data Science with Python. Park, Andrew 2020

English [en] · EPUB · 0.5MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.59728

English [en] · FB2 · 2.3MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.59728

lgli/CODING, MARK [CODING, MARK] - Machine Learning with Python: A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python. Neural Networks and Data Science from Pre-Processing to Deep Learning (2020, ).lit

English [en] · LIT · 1.7MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11048.0, final score: 48.586502

lgli/Samuel Burns & chenjin5.com - Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) (2018, cj5).mobi

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) Samuel Burns & chenjin5.com cj5, 2018

English [en] · MOBI · 1.2MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11050.0, final score: 48.58455

upload/newsarch_ebooks_2025_10/2020/10/31/Python for Data Science_ 2 Books in 1. A Practical Beginner’s Guide to learn Python Programming, introducing into Data Analytics, Machine Learning, Web Development, with Hands-on Projects.epub

Python for Data Science: 2 Books in 1. A Practical Beginner’s Guide to learn Python Programming, introducing into Data Analytics, Machine Learning, Web Development, with Hands-on Projects THOMPSON, ERICK 2020

English [en] · EPUB · 2.5MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

base score: 11060.0, final score: 48.56112

lgli/Samuel Burns - Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) (2018, ).epub

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) Samuel Burns 2018

English [en] · EPUB · 0.7MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11058.0, final score: 48.55778

lgli/Richard Moore - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science (2019, ).mobi

Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science Richard Moore 2019

English [en] · MOBI · 1.7MB · 2019 · 📕 Book (fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.55778

English [en] · LIT · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11048.0, final score: 48.55778

English [en] · AZW3 · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.5073

lgli/CODING, MARK - Machine Learning with Python: A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python. Neural Networks and Data Science from Pre-Processing to Deep Learning (2020, ).fb2

English [en] · FB2 · 2.4MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.493206

English [en] · LIT · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11048.0, final score: 48.48852

English [en] · AZW3 · 1.7MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

base score: 11053.0, final score: 48.48852

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