Advanced Machine Learning with R: Apply & Predict

本课程是 AI Machine Learning with R & Python Projects 专项课程的一部分

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

4个模块

深入了解一个主题并学习基础知识。

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Apply clustering, Naive Bayes, PCA, and neural networks in R.
Forecast time series with ARIMA, Prophet, and boosting methods.
Implement market basket analysis and optimize predictive models.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

16 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Machine Learning with R & Python Projects 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

By the end of this course, learners will be able to apply clustering algorithms, implement Naive Bayes classifiers, analyze text with supervised learning models, reduce dimensionality with PCA, and design foundational neural networks. They will also evaluate time series patterns, forecast using ARIMA and Prophet, optimize predictive performance with gradient boosting, and uncover associations through market basket analysis.

This course equips learners with advanced machine learning techniques using R, combining theoretical knowledge with hands-on implementation. Unlike traditional courses, it integrates clustering, supervised models, dimensionality reduction, neural networks, and advanced forecasting in a single structured program. Through practical coding examples and real-world case studies, participants will strengthen their ability to preprocess data, choose appropriate algorithms, and interpret results effectively. What makes this course unique is its balance of classic statistical foundations and modern ML applications, empowering learners to transition from exploratory analysis to building production-ready models. Professionals, data analysts, and aspiring data scientists will benefit from mastering advanced techniques that enhance both accuracy and interpretability in predictive modeling.

This module introduces unsupervised and probabilistic learning methods in R, focusing on clustering with K-Means and classification with Naive Bayes. Learners explore how to group unlabeled data into meaningful clusters and apply Bayes’ theorem to text and categorical data. Practical examples in R reinforce understanding of cluster visualization, probability computations, and classification accuracy.

涵盖的内容

12个视频3个作业

12个视频总计122分钟

Introduction to Kmeans Clustering 12分钟
Kmeans Elbow Point and Dataset 11分钟
Example of Kmeans Dataset 11分钟
Creating a Graph for Kmeans Clustering 11分钟
Creating a Graph for Kmeans Clustering Continues 7分钟
Aggregation Function of Clustering 9分钟
Conditional Probability with Bayes Algorithm 10分钟
Venn Diagram Naive Bayes Classification 9分钟
Component OF Bayes Theorem using Frequency Table 11分钟
Naive Bayes Classification Algorithm and Laplace Estimator 9分钟
Example of Naive Bayes Classification 9分钟
Example of Naive Bayes Classification Continues 11分钟

3个作业总计50分钟

K-Means Clustering 10分钟
Naive Bayes Classification 10分钟
Graded - Clustering and Bayesian Models 30分钟

This module explores advanced supervised learning techniques in R, including text mining with Naive Bayes and classification with Support Vector Machines. Learners analyze word frequency patterns, build document-term matrices, and develop spam detection models. They further master SVM concepts such as linear and nonlinear classification, the kernel trick, and RBF applications for optical character recognition (OCR).

涵盖的内容

9个视频3个作业

9个视频总计80分钟

Spam and Ham Messages in Word Cloud 9分钟
Implementation of Dictionary and Document Term Matrix 7分钟
Executes the Function Naive Bayes 9分钟
Support Vector Machine with Black Box Method 9分钟
Linearly and Non- Linearly Support Vector Machine 10分钟
Kernal Trick 10分钟
Gaussian RBF Kernal and OCR with SVMs 10分钟
Examples of Gaussian RBF Kernal and OCR with SVMs 8分钟
Summary of Support Vector Machine 8分钟

3个作业总计50分钟

Text Mining with Naive Bayes 10分钟
Support Vector Machines 10分钟
Graded - Advanced Supervised Learning 30分钟

This module focuses on techniques to simplify complex datasets and build predictive models with neural networks. Learners explore feature selection and extraction methods, apply Principal Component Analysis (PCA), and interpret eigenvalues and eigenvectors in R. The module concludes with neural network foundations, covering activation functions, topology, and weight adjustment for adaptive learning.

涵盖的内容

17个视频4个作业

17个视频总计166分钟

Feature Selection Dimension Reduction Technique 10分钟
Feature Extraction Dimension Reduction Technique 10分钟
Dimension Reduction Technique Example 9分钟
Dimension Reduction Technique Example Continues 8分钟
Introduction Principal Component Analysis 11分钟
Steps of PCA 11分钟
Steps of PCA Continues 9分钟
Eigen Values 9分钟
Eigen Vectors 8分钟
Principal Component Analysis using Pr-Comp 10分钟
Principal Component Analysis using Pr-Comp Continues 9分钟
C Bind Type in PCA 9分钟
R Type Model 13分钟
Black Box Method in Neural Network 9分钟
Characteristics of a Neural Networks 9分钟
Network Topology of a Neural Networks 11分钟
Weight Adjustment and Case Update 12分钟

4个作业总计60分钟

Feature Selection and PCA 10分钟
PCA Advanced 10分钟
Neural Network Foundations 10分钟
Graded - Dimensionality Reduction and Neural Networks 30分钟

This module integrates advanced applications of machine learning in R, including time series forecasting, boosting methods, and market basket analysis. Learners develop forecasting models, apply ARIMA and Prophet for stock prediction, and implement gradient boosting to improve accuracy. The module concludes with association rule mining and an overview of emerging machine learning trends.