Top 10 Machine Learning Algorithms for Beginners

最新推荐文章于 2024-01-08 01:23:15 发布
最新推荐文章于 2024-01-08 01:23:15 发布
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分类专栏： Reference 文章标签： ML 机器学习 python
机器学习之十大经典算法（三） CART树算法
机器之我心的博客
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        一、CART树简介：        ClassificationAnd Regression Tree(CART)是决策树的一种，并且是非常重要的决策树，属于Top Ten Machine Learning Algorithm。顾名思义，CART算法既可以用于创建分类树（ClassificationTree），也可以用于创建回归树（Regression Tree）、模型树（Mode...
机器学习经典算法详解及Python实现--CART分类决策树、回归树和模型树
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Classification And Regression Tree(CART)是一种很重要的机器学习算法，既可以用于创建分类树（Classification Tree），也可以用于创建回归树（Regression Tree），本文介绍了CART用于离散标签分类决策和连续特征回归时的原理。决策树创建过程分析了信息混乱度度量Gini指数、连续和离散特征的特殊处理、连续和离散特征共存时函数的特殊处理和后剪枝；用于回归时则介绍了回归树和模型树的原理、适用场景和创建过程。个人认为，回归树和模型树可以被看做“群落分类
Top 10 Machine Learning Algorithms For Beginners
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Linear Regression Logistic regression KNN Classification Support Vector Machine (SVM) Decision Trees Random Forest Artificial Neural Network K-means Clustering Naive Bayes theorem Recur...
轻松看懂机器学习十大常用算法 (Machine Learning Top 10 Commonly Used Algorithms)
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原文出处：不会停的蜗牛通过本篇文章可以对ML的常用算法有个常识性的认识，没有代码，没有复杂的理论推导，就是图解一下，知道这些算法是什么，它们是怎么应用的，例子主要是分类问题。每个算法都看了好几个视频，挑出讲的最清晰明了有趣的，便于科普。以后有时间再对单个算法做深入地解析。今天的算法如下：决策树随机森林算法逻辑回归SVM朴素贝叶斯K最近邻算法K均值算法Adaboost 算法神经网络马尔可夫1...
翻译：机器学习 数据科学家 top 10 十大算法
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10-25 600
说明 本文介绍了机器学习新手需要了解的 10 大算法，包括线性回归、Logistic 回归、朴素贝叶斯、K 近邻算法等。 在机器学习中，有一种叫做「没有免费的午餐」的定理。简而言之，它指出没有任何一种算法对所有问题都有效，在监督学习（即预测建模）中尤其如此。 例如，你不能说神经网络总是比决策树好，反之亦然。有很多因素在起作用，例如数据集的大小和结构。 因此，你应该针对具体问题尝试多种不同算法，并留出一个数据「测试集」来评估性能、选出优胜者。 当然，你尝试的算法必须适合你的问题，也就是选择正确的机器学习任务。
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Machine.Learning.for.Absolute.Beginners.pdf
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MACHINE LEARNING DATA MINING MACHINE LEARNING TOOLS MACHINE LEARNING CASE STUDIES ONLINE ADVERTISING GOOGLE’S MACHINE LEARNING MACHINE LEARNING TECHNIQUES INTRODUCTION REGRESSION SUPPORT VECTOR ...
Mastering Machine Learning Algorithms 2018
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Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. 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 supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who This Book Is For This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. Table of Contents Machine Learning Model Fundamentals Introduction to Semi-Supervised Learning Graph-based Semi-Supervised Learning Bayesian Networks and Hidden Markov Models EM algorithm and applications Hebbian Learning Advanced Clustering and Feature Extraction Ensemble Learning Neural Networks for Machine Learning Advanced Neural Models Auto-Encoders Generative Adversarial Networks Deep Belief Networks Introduction to Reinforcement Learning Policy estimation algorithms
[machine_learning_mastery系列]Master_Machine_Learning_Algorithms.pdf
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Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to describe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.
Machine-Learning-Algorithms-Second-Edition-master.zip
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机器学习算法第二版代码，然而，许多之前的机器学习模型往往只关注每个样本的特征，而没有考虑到样本之间的关系数据或没有很好的方法来利用和建模这些关系数据。图机器学习就应运而生了。近年来图逐渐变成机器学习的一个核心热门领域，拥有巨大的理论与实际应用发展前景。
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Mastering Machine Learning: A Comprehensive Guide to Smart Analytics
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This book consists of six chapters, which can be grouped into three subjects. The first subject is Machine Learning and takes place in Chapter 1. Deep Learning stems from Machine Learning. This implies that if you want to understand the essence of Deep Learning, you have to know the philosophy behind Machine Learning to some extent. Chapter 1 starts with the relationship between Machine Learning and Deep Learning, followed by problem solving strategies and fundamental limitations of Machine Learning. The detailed techniques are not introduced yet. Instead, fundamental concepts that applies to both the neural network and Deep Learning will be covered. The second subject is artificial neural network. Chapters 2-4 focuses on this subject. As Deep Learning is a type of Machine Learning that employs a neural network, the neural network is inseparable from Deep Learning. Chapter 2 starts with the fundamentals of the neural network: principles of its operation, architecture, and learning rules. It also provides the reason that the simple single-layer architecture evolved to the complex multi-layer architecture. Chapter 3 presents the backpropagation algorithm, which is an important and representative learning rule of the neural network and also employed in Deep Learning. This chapter explains how cost functions and learning rules are related and which cost functions are widely employed in Deep Learning. Chapter 4 introduces how to apply the neural network to classification problems. We have allocated a separate section for classification because it is currently the most prevailing application of Machine Learning. For example, image recognition, one of the primary applications of Deep Learning, is a classification problem. The third topic is Deep Learning. It is the main topic of this book as well. Deep Learning is covered in Chapters 5 and 6. Chapter 5 introduces the drivers that enables Deep Learning to yield excellent performance. For a better understanding, it starts with the history of barriers and solutions of Deep Learning. Chapter 6 covers the convolution neural network, which is representative of Deep Learning techniques. The convolution neural network is second-to-none in terms of image recognition. This chapter starts with an introduction of the basic concept and architecture of the convolution neural network as it compares with the previous image recognition algorithms. It is followed by an explanation of the roles and operations of the convolution layer and pooling layer, which act as essential components of the convolution neural network. The chapter concludes with an example of digit image recognition using the convolution neural network and investigates the evolution of the image throughout the layers.
mastering machine learning algorithms 2nd pdf
07-07
### 回答1： 《Mastering Machine Learning Algorithms 2nd PDF》是一本深入探讨机器学习算法的书籍。该书由一位资深的机器学习专家撰写，提供了最新的算法实现和应用案例。 这本书首先介绍了机器学习的基本概念和原理，包括监督学习、无监督学习和强化学习等。然后，它详细介绍了各种经典和现代的机器学习算法，如线性回归、逻辑回归、决策树、随机森林、支持向量机、神经网络等。对于每个算法，书中都提供了清晰的定义、算法步骤和实现细节。 此外，这本书还强调了实践的重要性，通过大量的案例和实例，帮助读者将理论知识应用到实际问题解决中。书中还提供了示例代码和数据集，使读者能够快速上手，并通过实际操作加深对算法的理解。 《Mastering Machine Learning Algorithms 2nd PDF》还提供了关于算法优化和改进的内容，帮助读者了解如何选择适合特定问题的算法，并对算法进行调优以达到更好的性能。 总的来说，这本书通过全面而深入的介绍了机器学习算法，帮助读者成为机器学习领域的专家。它适合那些对机器学习感兴趣的学生、研究人员和从业者，希望深入了解和应用各种机器学习算法的人群。无论是初学者还是有一定机器学习基础的人都能从中受益，并获得提高自己技能的机会。 ### 回答2： 《机器学习算法精要 第2版》是一本介绍掌握机器学习算法的重要书籍。本书的目的是帮助读者理解并有效地应用不同的机器学习算法。 这本书主要分为三个部分。第一部分首先介绍了机器学习的基本概念和术语，如监督学习、非监督学习和强化学习等。接着，书中详细介绍了各种常见的机器学习算法，包括线性回归、逻辑回归、决策树、支持向量机和朴素贝叶斯等。对于每个算法，书中提供了清晰的解释和算法实现的代码示例，并且还讨论了该算法的优势和限制。 第二部分涵盖了进阶的机器学习算法，如集成学习、神经网络和深度学习。这些算法通常用于处理更复杂的问题，例如图像识别和自然语言处理。书中详细介绍了这些算法的原理和实现方法，并提供了代码示例和实际案例来帮助读者更好地理解和应用这些算法。 第三部分聚焦于特定的机器学习应用领域，如推荐系统、文本分类和时间序列分析等。这些应用领域的研究一直在发展和改进，本书介绍了最新的研究成果和方法。读者可以通过学习这些实际应用案例来更好地理解机器学习算法在不同领域的应用，并且可以将这些知识应用到自己的项目中。 总的来说，这本《机器学习算法精要 第2版》是一本很好的机器学习教材。无论是初学者还是有经验的机器学习工程师，都可以从中受益。通过学习这本书，读者可以掌握不同的机器学习算法，并且能够灵活地应用这些算法解决实际问题。
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