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2023 ML Mastery: Complete machine learning with R Studio

In this course, you will delve into various Machine Learning with R studio models using the R programming language within the RStudio environment. You will explore essential topics such as Linear Regression, Logistic Regression, Decision Trees, XGBoost, and Support Vector Machines (SVM), among others. By utilizing R and RStudio, you will gain hands-on experience in implementing these models and analyzing their outcomes. This practical approach will enhance your understanding and proficiency in Machine Learning using the R programming language.

Created by start-tech-academy

What You’ll learn in complete machine learning with R studio

  • Gain the ability to address real-life problems using Machine Learning techniques.
  • Familiarize yourself with fundamental Machine Learning models, including Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN).
  • Explore advanced Machine Learning models, such as Decision Trees, XGBoost, Random Forest, and Support Vector Machines (SVM).
  • Develop a solid understanding of basic statistics and fundamental concepts in Machine Learning.
  • Learn to perform basic statistical operations and implement Machine Learning models in the R programming language.
  • Acquire in-depth knowledge of data collection and data preprocessing for Machine Learning tasks.
  • Learn how to effectively translate business problems into Machine Learning problems, enabling you to solve them using appropriate techniques.

Through this comprehensive course, you will gain the practical skills and theoretical understanding necessary to apply Machine Learning techniques to solve real-world problems successfully.

machine learning with R studio

Course content:

  1. Welcome to the course.
  2. R Studio Setup and Crash Course
  3. Basics of Statistics.
  4. Intorduction to Machine Learning.
  5. Data Preprocessing for Regression Analysis.
  6. Linear Regression Model.
  7. Regression models other than OLS.
  8. Introduction to the classification Models.
  9. Logistic Regression.
  10. Linear Discriminant Analysis.
  11. K-Nearest Neighbors.
  12. Comparing results from 3 models.
  13. Simple Decision Trees.
  14. Simple Classification Tree.
  15. Ensemble technique 1 – Bagging.
  16. Ensemble technique 2 – Random Forest.
  17. Advanced Ensemble Techniques – GBM, AdaBoost, and XGBoost
  18. Support Vector Machines.
  19. Support Vector Classifier.
  20. Support Vector Machines.
  21. Creating Support Vector Machine Model in R.
  22. Bonus Lecture

Requirements:

To participate in this course, students will be required to install the R programming language and RStudio software. However, rest assured that we have dedicated a separate lecture specifically designed to guide you through the installation process. This step-by-step tutorial will ensure that you have the necessary software set up on your computer to fully engage in the course content.

Description:

Are you searching for a comprehensive Machine Learning course that can help you establish a successful career in the field of Data Science, Machine Learning, R, and Predictive Modeling? Look no further, as you’ve found the perfect Machine Learning course!

Upon completing this course, you will gain the following abilities:

  • Build predictive Machine Learning models confidently using R to address business problems and formulate effective business strategies.
  • Successfully tackle Machine Learning-related interview questions.
  • Participate and excel in online Data Analytics competitions such as Kaggle.

This course offers a detailed table of contents, providing insights into the range of Machine Learning models you will learn.

Why should you select this course?

While many courses primarily focus on demonstrating how to run analyses, we believe that understanding what happens before and after running an analysis is equally vital. Thus, this course not only emphasizes the analysis process but also emphasizes the importance of acquiring the right data and performing adequate pre-processing. Furthermore, it equips you with the skills to assess the quality of your model and interpret the results effectively, enabling you to make informed decisions that benefit your business.

What qualifies us to teach you?

This course is taught by Abhishek and Pukhraj, who are managers in a renowned Global Analytics Consulting firm. With their extensive experience, they have helped numerous businesses solve their problems using machine learning techniques in R and Python. Their expertise and practical knowledge have been incorporated into this course, ensuring that it covers the essential aspects of data analysis.

Abhishek and Pukhraj, the creators of highly acclaimed online courses, have garnered over 150,000 enrollments and received numerous 5-star reviews from their satisfied students. Here are a couple of testimonials from their previous students:

  • Joshua expressed his satisfaction with the course, stating that he appreciates the clear explanations that are easily understandable even for someone without prior knowledge in the field.
  • “Thank you, Author, for this wonderful course. Your praise is greatly appreciated, and I’m glad you find the course invaluable.” – Daisy

Our Commitment

Teaching our students is our utmost priority, and we are fully dedicated to it. If you have any questions about the course content, machine learning, R, predictive modeling, practice sheets, or any related topics, you can always post a question in the course or send us a direct message for personalized assistance.

Practical Learning Opportunities

Each lecture is accompanied by class notes for you to follow along. Additionally, quizzes are provided to evaluate your understanding of machine learning, R, and predictive modeling concepts. Furthermore, every section includes a practice assignment that allows you to apply your learning in practical scenarios related to machine learning, R, and predictive modeling.

FAQs for Getting Started in Machine Learning

The course also addresses popular frequently asked questions (FAQs) that beginners often have when embarking on their machine learning journey. Some of these FAQs include:

  1. What is Machine Learning?
  2. What are the steps to build a Machine Learning model?
  3. Why should you use R for Machine Learning?
  4. What are the key advantages of using R compared to Python?
  5. How do Data Mining, Machine Learning, and Deep Learning differ from each other?

By addressing these FAQs, the course ensures that you have a solid foundation in understanding the key concepts and terminology associated with machine learning, R, and predictive modeling.

In summary, this course combines the expertise and practical experience of Abhishek and Pukhraj, providing you with a comprehensive learning experience in machine learning, R, and predictive modeling.

You can also check other courses as well: Python Essentials 2023: Machine Learning, Data Science, and Deep Learning

This course is suitable for:

  1. Individuals pursuing a career in data science.
  2. Working professionals beginning their data journey.
  3. Statisticians who want practical experience in machine learning.

Here is the decoded magnet link of the course.

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Magnet link 1

Frequently Asked Questions (FAQs)

Certainly! Here are some frequently asked questions (FAQs) addressed in the “Machine Learning using R” course:

Q: What is Machine Learning?

A: This question introduces the concept of machine learning and provides an understanding of how it enables computers to learn from data and make predictions or decisions without being explicitly programmed.

Q: What are the steps to build a Machine Learning model?

A: This question outlines the general steps involved in building a machine learning model, including data collection, data preprocessing, feature selection, model training, model evaluation, and deployment.

Q: Why should you use R for Machine Learning?

A: This question highlights the advantages of using R programming language for machine learning, such as its extensive collection of libraries and packages specifically designed for data analysis, statistical modeling, and machine learning tasks.

Q: What are the major advantages of using R over Python?

A: This question discusses the strengths of R compared to Python for machine learning, such as R’s rich ecosystem of statistical packages, its focus on data analysis and visualization, and its popularity in academic and research communities.

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

A: This question clarifies the distinctions between data mining, machine learning, and deep learning. Data mining involves discovering patterns and insights from large datasets, machine learning focuses on building predictive models using algorithms, and deep learning is a subset of machine learning that deals with neural networks and complex hierarchical representations.

These FAQs are designed to provide a solid foundation and address common questions beginners may have when starting their journey in machine learning using R.

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