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Machine Learning with R Mini Bootcamp

Machine Learning with R Mini Bootcamp

  • About

    R is now considered one of the most popular analytics tools in the world. This Machine Learning with R mini bootcamp dives into the basics of machine learning using this approachable, and well-known, programming language. During this mini bootcamp, You’ll learn about Supervised vs Unsupervised Learning, and you will also look into Dimensionality Reduction & Collaborative Filtering.

    This mini bootcamp also provides you to look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

    Who is this course for:

    • Anyone who wants an introduction to Machine Learning with R.
    • Anyone who is interested in building ML applications in R.

    Course Requirements:

    • Need to know theory of ML.
    • Basic Math knowledge.
  • Curriculum
    • Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

    • 1. Machine Learning Languages, Types, and Examples


      2. Machine Learning vs Statistical Modelling


      3. Supervised vs Unsupervised Learning


      4. Supervised Learning Classification


      5. Unsupervised Learning

    • Module 2 – Supervised Learning I

    • 1. K-Nearest Neighbors


      2. Decision Trees


      3. Random Forests


      4. Reliability of Random Forests


      5. Advantages & Disadvantages of Decision Trees

    • Module 3 – Supervised Learning II

    • 1. Regression Algorithms


      2. Model Evaluation


      3. Model Evaluation: Overfitting & Underfitting


      4. Understanding Different Evaluation Models

    • Module 4 – Unsupervised Learning

    • 1. K-Means Clustering plus Advantages & Disadvantages


      2. Hierarchical Clustering plus Advantages & Disadvantages


      3. Measuring the Distances Between Clusters – Single Linkage Clustering


      4. Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering


      5. Density-Based Clustering

    • Module 5 – Dimensionality Reduction & Collaborative Filtering

    • 1. Dimensionality Reduction: Feature Extraction & Selection


      2. Collaborative Filtering & Its Challenges

  • Schedule
    • Start learning the R with Machine Learning with outside of business hours schedule!
    • The 12 hours of schedule is as follows:
    • March 14 – 21 – 28 and April 4
    • Saturdays, from 2:00 pm to 5:00 pm
  • Location

    The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.

  • Online

    Machine Learning with R Mini Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.

  • Tuition
    Tuition fee

    Machine Learning with R Mini Bootcamp has a $300 tuition fee.

    For the “Early Bird” applicants (January 15 – March 7), the tuition fee is $250.

    Payment process

    After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.

    Cancellation

    If you’re not satisfied with the course you may cancel your application.

  • Application

    The application process starts at magnimindacademy.com. You can view the course pages and learn more about your intended course. You can apply by clicking the “Buy now” button and then fill out the application form.

  • Instructor

    Mr. Clem Wang has been a Data Scientist for 15 years, working at both large companies like Yahoo and Microsoft, and a bunch of startups. He’s used both Python and R professionally. In a previous life, he’s been involved with QA’ing compilers and interpreters, so he has some insights in the inner workings of R and Python.

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