Complete Machine Learning with R Studio – ML for 2021

本课程涵盖了通过线性回归解决业务问题时应采取的所有步骤。大多数课程只专注于讲授如何运行分析，但是我们认为运行分析之前和之后发生的事情更为重要，即在运行分析之前，拥有正确的数据并对其进行一些预处理非常重要。在进行分析之后，您应该能够判断模型的质量，并解释结果，从而真正可以帮助您的业务。

该课程由Abhishek和Pukhraj教授。作为Global Analytics Consulting公司的经理，我们已经使用机器学习技术帮助企业解决了他们的业务问题，并且利用我们的经验在本课程中纳入了数据分析的实际方面。

为什么要使用R进行机器学习？

理解R是机器学习职业所需的宝贵技能之一。以下是您应该在R中学习机器学习的一些原因

1.它是顶级技术公司中机器学习的流行语言。几乎所有人都雇用使用R的数据科学家。例如，Facebook使用R对用户发布的数据进行行为分析。Google使用R来评估广告效果并做出经济预测。顺便说一句，不仅仅是技术公司：R在分析和咨询公司，银行和其他金融机构，学术机构和研究实验室中使用，并且几乎所有其他地方都需要对数据进行分析和可视化。

2.学习数据科学基础在R中可以说很容易。R有一​​个很大的优势：R是专门为数据处理和分析而设计的。

3.惊人的包装，让您的生活更轻松。因为R在设计时就考虑了统计分析，所以它拥有一个由数据包和其他资源组成的绝佳生态系统，非常适合数据科学。

4.健壮且不断发展的数据科学家和统计学家社区。随着数据科学领域的迅猛发展，R随之发展，成为世界上增长最快的语言之一（按StackOverflow衡量）。这意味着在您通过R中的项目进行工作时，很容易找到问题的答案和社区指导。

5.在您的工具箱中放入另一个工具。没有一种语言会成为每种工作的正确工具。在您的库中添加R将使某些项目更容易–当然，当您在寻找数据科学领域的工作时，它也将使您成为更具灵活性和适销对路的员工。

数据挖掘，机器学习和深度学习之间有什么区别？

简而言之，机器学习和数据挖掘使用与数据挖掘相同的算法和技术，只是预测的种类有所不同。数据挖掘发现了以前未知的模式和知识，而机器学习则复制了已知的模式和知识，并进一步将该信息自动应用于数据，决策和行动。

另一方面，深度学习使用先进的计算能力和特殊类型的神经网络，并将其应用于大量数据，以学习，理解和识别复杂的模式。自动语言翻译和医学诊断是深度学习的示例。

Duration: 12h 51m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 5.96 GB
Genre: eLearning | Language: English

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language – R studio

What you’ll learn
Learn how to solve real life problem using the Machine learning techniques
Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
Understanding of basics of statistics and concepts of Machine Learning
How to do basic statistical operations and run ML models in R
Indepth knowledge of data collection and data preprocessing for Machine Learning problem
How to convert business problem into a Machine learning problem

Requirements
Students will need to install R and R studio software but we have a separate lecture to help you install the same

Description
You’re looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?

You’ve found the right Machine Learning course!

After completing this course you will be able to:

· Confidently build predictive Machine Learning models to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions

Check out the table of contents below to see what all Machine Learning models you are going to learn.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman – Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience