Applied Data Science and Machine Learning in R for Beginners

在本课程中，我们从 R 编程的基础开始，提供了对数据科学方法和技术以及机器学习的基本理解。完成本课程后，对数据科学和机器学习感兴趣的新初学者将能够自学。这意味着学生将能够根据他们在这里学到的东西建立高级知识。

我们不仅将使用 R 和不同包中的预定义函数，还将学习如何从头开始构建机器学习算法来定义我们自己的函数。

在本课程中，我们将涵盖诸如 R 编程、数据科学和机器学习简介、数据理解、数据预处理、数据清洗以及如何应用数据科学工具来预处理和分析数据（结构化和非结构化数据）等主题) 并构建预测模型、执行聚类、PCA 等。

机器学习主题将包括监督学习和无监督学习。在监督学习中，我们将涵盖线性回归、逻辑回归、时间序列和预测、文本分析（自然语言处理的一部分）、神经网络、支持向量机、购物篮分析（关联规则）等主题。

作为无监督学习的一部分，我们将进行聚类，例如层次聚类和非层次聚类。我们将关注诸如 K-means、软和硬聚类、相似函数、凝聚方法（自下而上）、分裂方法（自上而下）等主题。我们还将涵盖诸如主成分分析 (PCA) 等主题，以及如何在现实生活中使用这两者。

我们将研究各种主题的不同示例。最后，我们将使用不同的数据集，以及所有的代码和操作，完成每个主题的各种实验课程。

Learn Data Science Methods and Techniques in R. Build Machine Learning Algorithms from Scratch. Gain Insight into Data

Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 61 lectures (9h 8m) | Size: 2.12 GB

What you’ll learn

Data Science Concept
R Programming Basics
Machine Learning Basics
Predictive Model Building Basics
Basics of text analytics
Basics of Time Series and Forecasting

Requirements

An overview of data science and machine learning will be an added advantage, but not mandatory
Basic knowledge of programming will be an added advantage, but not mandatory

Description

In this course, we have provided the basic understanding of data science methods and techniques and machine learning, beginning with the basics of R programming. Having gone through this course, a new beginner, who has interest in data science and machine learning, will be able to sail through on their own. What it means is that students will be able to build the advanced knowledge based on what they learn here.

We will not only use the predefined functions in R and different packages, but we will also learn how to build machine learning algorithms from scratch defining our own functions.

In this course we will cover topics such as, R programming, introduction to data science and machine learning, understanding of data, preprocessing of data, cleansing of data and how to apply data science tools to preprocess and analyze data (structured as well as unstructured) and build predictive models, perform clustering, PCA, etc.

The machine learning topics would include, supervised learning and unsupervised learning. In supervised learning we will cover topics such as linear regression, logistics regression, time series and forecasting, text analytics (part of natural language processing), neural network, support vector machine, market basket analysis (association rules).

As part of unsupervised learning, we will go through clustering, such as hierarchical clustering and non-hierarchical clustering. We will focus on topics such as K-means, soft and hard clustering, similarity functions, agglomerative methods (bottom up), divisive methods (top-down). We will also cover topics such as principal component analysis (PCA), and how to use both in real life.

We will look at different examples of various topics. At the end, we will go through various lab sessions on each topic, using different datasets, with all the codes and operations.

Who this course is for
Beginners who have interest in learning about data science and machine learning
Basic understanding of how programming works and can grasp R programming will be an added advantage, but not mandatory