在本课程中,我们将介绍机器学习的概念以及不同学习方法的分类,如监督学习和非监督学习。我们还介绍了强化学习。我们提供流行的技术,并用Python实现它们。我们从决策树方法开始。我们简单地用所有需要的数学工具,如熵来描述它。我们用Python实现了它们,并解释了如何提高精确度。我们为分类问题提供了一个合适的真实场景。线性回归使用简单的现实生活中的例子来教授。我们提出了L2误差估计,并解释了我们如何可以使用梯度优化最小化误差。Supervised Machine Learning Principles And Practices-Python

这是使用Python库实现的。本文还给出了逻辑回归方法的一个例子,并用Python实现。最近邻法用例子解释,并用Python实现。支持向量机(SVM)是一种流行的监督学习模型,可用于分类或回归。这种方法适用于高维空间(特征向量中的许多特征),并且可以有效地用于小数据集。当在数据集上训练时,该算法可以很容易地有效地对新的观察结果进行分类。我们还提出了一些新的方法。贝叶斯分类模型用于大型有限数据集。这是一种使用直接非循环图分配类别标签的方法。该图包括一个父节点和多个子节点。并且假设每个子节点是独立的并且与父节点分离。由于ML中的监督学习模型有助于以简单明了的方式构建分类器,因此它非常适合非常小的数据集。该模型利用了常见的数据假设,例如每个属性都是独立的。然而有了这样的简化,这种算法可以很容易地在复杂的问题上实现。
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.62 GB | Duration: 3小时 51分钟

Algorithms and Practical Examples in Python

What you’ll learn
Understand the mathematics behine Machine Learning
Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc.
Python Code for Supervised learning models
Creating a ML model and solving for a given set of data.

Requirements
Basic Mathematics, Programming foundations

Description
In this course, we present the concept of machine learning and the classification of different methods of learning such as Supervised and Unsupervised Learning. We also present reinforcement learning. We offer popular techniques and implement them in Python. We begin with the Decision Tree method. We present this simply with all the required mathematical tools such as entropy. We implement them in Python and explain how the accuracy can be improved. We offer the classification problem with a suitable real-life scenario. Linear Regression is taught using simple real-life examples. We present the L2 Error estimation and explain how we can minimize the error using gradient optimization. This is implemented using the Python library. We also offer the Logistic Regression method with an example and implement in Python. The Nearest Neighbourhood approach is explained with examples and implemented in Python. Support Vector Machines (SVM) are a popular supervised learning model that you can use for classification or regression. This approach works well with high-dimensional spaces (many features in the feature vector) and can be used with small data sets effectively. When trained on a data set, the algorithm can easily classify new observations efficiently. We also present a few more methods. The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent. As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.

Overview
Section 1: Introduction

Lecture 1 Learning by Observation

Lecture 2 Learning Agents

Section 2: Forms of Learning

Lecture 3 Forms of Learning – Inductive Learning

Section 3: Inductive Learning Methods

Lecture 4 Supervised Learning

Lecture 5 Unsupervised Learning

Lecture 6 Reinforcement Learning

Section 4: Decision Tree Model

Lecture 7 Introduction to Decision Trees

Lecture 8 Decision Tree Construction Algorithm

Lecture 9 Mathematical Constructs for Decision Tree – Entropy, Remainder and Info gain

Lecture 10 Decision Tree Code using sklearn – Syntax explained

Lecture 11 Decision Tree – Python Lab

Lecture 12 Decision Tree Testing the Model Python Lab

Section 5: Linear Regression

Lecture 13 Linear Regression – Gradient Descent – Concept and Algorithm

Lecture 14 Linear Regression – Gradient Descent – Multivariate

Lecture 15 Writing Python code using Skilearn

Lecture 16 Linear Regression – Python with Skilearn Practical Demonstration

Bachelor and Master Degree students,Machine Learning Programmers

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