Applied Machine Learning in R

课程使用 R 程序为您提供机器学习方面的实践培训。在课程结束时，您将了解如何使用最广泛的机器学习技术进行准确的预测并从您的数据中获得有价值的见解。

所有机器学习过程都在现实生活数据集上进行了现场详细解释。因此，您将快速进步并能够立即应用您的知识 – 无需通过痛苦的反复试验来弄清楚如何在 R 中实现这种或那种技术。在很短的时间内，您就可以拥有扎实的机器学习专业知识。

如果您想获得数据分析师、数据科学家、研究人员甚至软件工程师等工作，机器学习技能非常有价值。因此，现在可能是您注册本课程并立即开始构建您的机器学习能力的最佳时机！

让我们看看你将在这里学到什么。

首先，我们将讨论在执行机器学习之前你必须完全了解的一些基本概念。因此，我们将讨论有监督和无监督的机器学习技术、预测和推理之间的区别、回归和分类模型，最重要的是关于偏差-方差权衡，这是机器学习中的一个关键问题。

接下来我们将学习交叉验证。这是一个非常重要的话题，因为在机器学习中，我们必须能够在独立数据集（也称为首次看到的数据）上测试和验证我们的模型。因此，我们将介绍三种交叉验证方法的优缺点。

在前两个介绍部分之后，我们将开始研究监督机器学习技术。我们将从回归技术开始，其中响应变量是定量的。不，我们不会坚持使用您可能已经知道的经典 OLS 回归。我们将研究复杂的回归技术，如逐步回归（向前和向后）、惩罚回归（岭和套索）和偏最小二乘回归。当然，我们将使用实际数据集在 R 中演示所有这些。

之后我们将讨论分类技术，这在我们必须预测分类变量时非常有用。在这里，我们将研究逻辑回归（经典和套索）、判别分析（线性和二次）、朴素贝叶斯技术、K 最近邻、支持向量机、决策树和神经网络。

对于上述每种技术，演示文稿的结构如下：

* 简短易懂的理论介绍（无需复杂的数学）

* 如何在 R 中训练预测模型

* 如何测试模型以确保它对独立数据集进行良好的预测工作。

在最后一节中，我们将研究两种无监督机器学习技术：主成分分析和聚类分析。它们是强大的数据挖掘技术，可让您检测数据或变量中的模式。

对于每种技术，都提出了许多实践练习。通过做这些练习，你实际上将把你学到的东西应用到实践中。

本课程是您在几周内成为机器学习专家的机会！通过我的视频讲座，您会发现掌握主要的机器学习技术非常容易。一切都一步一步地实时显示，因此您可以随时复制任何程序。

Created by Bogdan Anastasiei | Last updated 12/2020

Duration: 8h 10m | 18 sections | 80 lectures | Video: 1280×720, 44 KHz | 1 GB
Genre: eLearning | Language: English + Sub

Get the essential machine learning skills and use them in real life situations

What you’ll learn
Understand the essential concepts related to machine learning
Perform model cross-validation to assess model stability on independent data sets
Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
Perform logistic regression and discriminant analysis
Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
Use neural networks to make predictions
Use principal components analysis to detect patterns in variables
Conduct cluster analysis to group observations into homogeneous classes

Requirements
Knowledge of the R programBasic knowledge of statistics and statistical analysis
Description
This course offers you practical training in machine learning, using the R program. At the end of the course you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from your data.

All the machine learning procedures are explained live, in detail, on real life data sets. So you will advance fast and be able to apply your knowledge immediately – no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time you can have a solid expertise in machine learning.

Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences today!

Let’s see what you are going to learn here.

First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we’ll talk about supervised and unsupervised machine learning techniques, about the distinctions between prediction and inference, about the regression and classification models and, above all, about the bias-variance trade-off, a crucial issue in machine learning.

Next we’ll learn about cross-validation. This is an all-important topic, because in machine learning we must be able to test and validate our model on independent data sets (also called first seen data). So we are going to present the advantages and disadvantages of three cross-validations approaches.

After the first two introductory sections, we will get to study the supervised machine learning techniques. We’ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backward), penalized regression (ridge and lasso) and partial least squares regression. And of course, we’ll demonstrate all of them in R, using actual data sets.

Afterwards we’ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we’ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), naïve Bayes technique, K nearest neighbor, support vector machine, decision trees and neural networks.

For each technique above, the presentation is structured as follows:

* a short, easy to understand theoretical introduction (without complex mathematics)

* how to train the predictive model in R

* how to test the model to make sure that it does a good prediction job on independent data sets.

In the last sections we’ll study two unsupervised machine learning techniques: principal component analysis and cluster analysis. They are powerful data mining techniques that allow you to detect patterns in your data or variables.

For each technique, a number of practical exercises are proposed. By doing these exercises you’ll actually apply in practice what you have learned.

This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it.

So click the “Enroll” button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future.

See you inside!
Who this course is for:Data analystsData scientistsResearchersStudents