Python编程无师自通

Machine Learning with Python (Theory and Implementation)

82篇顶会巨佬撰写的入门机器学习与深度学习的神书

Machine-Learning-With-Python

Machine-Learning-With-Python

Python Machine Learning. Machine Learning and Deep Learning with Py, scikit

Through exposure to the news and social media, you are probably aware of the fact that machine learning has become one of the most exciting technologies of our time and age. Large companies, such as Google, Facebook, Apple, Amazon, and IBM, heavily invest in machine learning research and applications for good reasons. While it may seem that machine learning has become the buzzword of our time and age, it is certainly not a fad. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. This is evident in talking to the voice assistant on our smartphones, recommending the right product for our customers, preventing credit card fraud, filtering out spam from our email inboxes, detecting and diagnosing medical diseases, the list goes on and on. If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you. However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming. Many practical books have been published in recent years that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning are a great way to dive into this field. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! In addition to offering a hands-on experience with machine learning using the Python programming languages and Python-based machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which is essential for using machine learning successfully. Thus, this book is different from a purely practical book; it is a book that discusses the necessary details regarding machine learning concepts and offers intuitive yet informative explanations of how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls. Currently, if you type "machine learning" as a search term in Google Scholar, it returns an overwhelmingly large number of publications—1,800,000. Of course, we cannot discuss the nitty-gritty of all the different algorithms and applications that have emerged in the last 60 years. However, in this book, we will embark on an exciting journey that covers all the essential topics and concepts to give you a head start in this field. If you find that your thirst for knowledge is not satisfied, this book references many useful resources that can be used to follow up on the essential breakthroughs in this field. If you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. If you have used machine learning techniques before and want to gain more insight into how machine learning actually works, this book is for you. Don't worry if you are completely new to the machine learning field; you have even more reason to be excited. Here is a promise that machine learning will change the way you think about the problems you want to solve and will show you how to tackle them by unlocking the power of data. Before we dive deeper into the machine learning field, let's answer your most important question, "Why Python?" The answer is simple: it is powerful yet very accessible. Python has become the most popular programming language for data science because it allows us to forget about the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action. We, the authors, can truly say that the study of machine learning has made us better scientists, thinkers, and problem solvers. In this book, we want to share this knowledge with you. Knowledge is gained by learning. The key is our enthusiasm, and the real mastery of skills can only be achieved by practice. The road ahead may be bumpy on occasions and some topics may be more challenging than others, but we hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this book, we will add many powerful techniques to your arsenal that will help us solve even the toughest problems the data-driven way.

Python Machine Learning 2nd Edition [Sebastian Raschka]

What you will learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis

Python Machine Learning

About This Book, Leverage Python' s most powerful open-source libraries for deep learning, data wrangling, and data visualization, Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms, Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets, Who This Book Is For, If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource., What You Will Learn, Explore how to use different machine learning models to ask different questions of your data, Learn how to build neural networks using Keras and Theano, Find out how to write clean and elegant Python code that will optimize the strength of your algorithms, Discover how to embed your machine learning model in a web application for increased accessibility, Predict continuous target outcomes using regression analysis, Uncover hidden patterns and structures in data with clustering, Organize data using effective pre-processing techniques, Get to grips with sentiment analysis to delve deeper into textual and social media data, Style and approach, Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Advanced-Machine-Learning-with-Python, 高级机器学习的代码库，python 发布，由Packt发布.zip

Advanced-Machine-Learning-with-Python, 高级机器学习的代码库，python 发布，由Packt发布 高级机器与 python 学习硬件软件列表章节号软件要求( 带版本)##Related 图书构建机器学习系统与 python 。主 python 数据...

Mastering Machine Learning with Python in six Steps

This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the “Six degrees of separation” theory that ...

基于Python的机器学习（Python-Real World Machine Learning）-2016年最新英文原版，0积分

基于Python的机器学习（Python-Real World Machine Learning）-2016年最新英文原版，0积分——全书983页，内容很丰富。 What this learning path covers？ Module 1, Python Machine Learning Cookbook, teaches you...

Advanced Machine Learning with Python ，使用Python进行高级及其学习

计算能力的不断进步（根据摩尔定律）已经开始使曾经主要是一门研究学科的机器学习，在商业环境中更加可行。这导致了新的应用程序和新的或重新发现的技术的爆炸式增长，使数据科学、人工智能和机器学习的模糊概念迅速...

Hands-On Data Science and Python Machine Learning

Hands-On Data Science and Python Machine Learning by Frank Kane English | 31 July 2017 | ISBN: 1787280748 | ASIN: B072QBVXGH | 420 Pages | AZW3 | 7.21 MB Key Features Take your first steps in the ...

Machine Learning in Python 高清

很好的一本书 值得推荐，本书主要包含以下主题：计算机内部结构的背景知识、列表和元组、字典和集合、迭代器和生成器、矩阵和矢量计算、并发、集群和工作队列等。最后，通过一系列真实案例展现了在应用场景中需要注意的问题。 本书适合初级和中级Python程序员、有一定Python语言基础想要得到进阶和提高的读者阅读

Practical Machine Learning with Python.pdf

The availability of affordable compute power enabled by Moore’s law has been enabling rapid advances in Machine Learning solutions and driving adoption across diverse segments of the industry. The ability to learn complex models underlying the real-world processes from observed (training) data through systemic, easy-to-apply Machine Learning solution stacks has been of tremendous attraction to businesses to harness meaningful business value. The appeal and opportunities of Machine Learning have resulted in the availability of many resources—books, tutorials, online training, and courses for solution developers, analysts, engineers, and scientists to learn the algorithms and implement platforms and methodologies. It is not uncommon for someone just starting out to get overwhelmed by the abundance of the material. In addition, not following a structured workflow might not yield consistent and relevant results with Machine Learning solutions.

Practical Machine Learning with Python (2018)

英文。2018版本。The authors of this book have leveraged their hands-on experience with solving real-world problems using Python and its Machine Learning ecosystem to help the readers gain the solid knowledge needed to apply essential concepts, methodologies, tools, and techniques for solving their own real-world problems and use-cases. Practical Machine Learning with Python aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical Machine Learning solutions.

Python Machine Learning and Deep Learning with Python

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

Python Machine Learning Machine Learning and Deep Learning

Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition 很受推荐

Mastering Machine Learning Algorithms 2018

Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in ...

Machine Learning for Developers-Packt Publishing(2017).pdf

RNNs),concluding with a synthetic introduction to more advanced machine learning techniques, such as GANs and reinforcement learning. This book is aimed at developers looking to finally grasp what ...

吴恩达,机器学习专项课程, Advanced Learning Algorithms第一周所有Python编程文件

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Practical Machine Learning (English Edition)

This book explores an extensive range of ... providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies.

Python Machine Learning Blueprints[July 2016]

Machine learning is rapidly becoming a fixture in our data-driven world. It is relied upon in fields as diverse as robotics and medicine to retail and publishing. In this book, you will learn how to ...

清华大学高级机器学习教学大纲

课程主要介绍高级机器学习的理论和相关算法；课程内容涵盖经典机器学习方法的回顾、概率图生成模型（Generative learning）和概率图判别模型（Discriminative learning），以机器学习模型的理论分析和算法的应用验证为基础

南京大学《高级机器学习》.rar

南京大学李宇峰老师在2021年开设的高级学习课程，以周志华老师西瓜书为教材，讲述了从模型选择、到强化学习等高级机器学习主题，可供学习参考。 目录内容： Lecture 1: Basic info for the course [Slide] Introduction [Slide] Lecture 2: Model selection [Slide] Lecture 3: Linear Model [Slide] Support Vector Machine [Slide] Neural Network [Slide] Decision Tree [Slide] Bayesian Model [Slide] Lecture 4: Ensemble Learning [Slide] Lecture 5: Clustering [Slide] Lecture 6: Dimension Reduction [Slide] Lecture 7: Feature Selection [Slide] Lecture 8: Semi-Supervised Learning [Slide] Lecture 9: Multi-Label Learning* [Slide] Lecture 10: Graphical Model [Slide] Lecture 11: Reinforcement Learning [Slide] 《机器学习》（西瓜书） 1-10章和11章之后的区别 • 1-10章，主要介绍了机器学习基本原理和经典 模型（机器学习是有理论基础，有直觉原理的） • 11章后，开始涉及复杂学习模型，应对复杂现 实数据问题（现实世界问题是复杂的）

Python Machine Learning (2nd) -2017-9

Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Sets - Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Embedding a Machine Learning Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with TensorFlow Going Deeper - The Mechanics of TensorFlow Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data using Recurrent Neural Networks

python machine learning

Machine learning for beginners starting from scratch

吴恩达,机器学习专项课程, Advanced Learning Algorithms第二周所有Python编程文件

吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第 二周所有jupyter notebook文件(包括实验室练习文件)

2020年机器学习深度学习下载地址.txt

李宏毅2020机器学习深度学习 P1. Machine Learning 2020_ Course Introduction P2. Rule of ML 2020 P3. Regression - Case Study P4. Basic Concept P5. Gradient Descent_1 P6. Gradient Descent_2 P7. ...

吴恩达,机器学习专项课程, Advanced Learning Algorithms第四周所有Python编程文件

吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第 四周所有jupyter notebook文件(包括实验室练习文件)

吴恩达,机器学习专项课程, Advanced Learning Algorithms第三周所有Python编程文件

吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第 三周所有jupyter notebook文件(包括实验室练习文件)

PythonMachineLearningBlueprint.pdf

Python Machine Learning Blueprints Intuitive data projects you can relate to An approachable guide to applying advanced machine learning methods to everyday problems Copyright © 2016 Packt Publishing