Machine Learning: Classification 2

The science of solving classification tasks

Ad-Hoc Course Registration:

  • Date: 18 – 21 January 2021
  • Time: 18.30 – 21.30
  • Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
  • Investment: Rp. 5.200.000
  • Date: 18 – 21 January 2021
  • Time: 18.30 – 21.30
  • Investment: Rp. 2.600.000


Course details :

Learn to apply the law of probabilities, boosting, bootstrap aggregation, k-fold cross-validation, ensembling methods, and a variety of other techniques as we build some of the most widely used machine learning algorithms today. Learn to add performance to your models using mathematically sound principles you’ll learn in this course.

We strongly recommend that you complete the Classification in Machine Learning 1 workshop prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.


  • Law of Probability

    Day 1

  • Naive Bayes

    Day 1

  • Practical and Performance Considerations

    Day 2

  • Decision Trees

    Day 2

  • Machine Learning Theories

    Day 3

  • High-Performance Machine Learning

    Day 3

  • Learn-by-Building

    Day 4

Course Producer

Samuel Chan

An  RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce.  Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.

Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.

Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.

4-Day Workshop Modules

Syllabus: Classification in Machine Learning 2

Module 1: Naive Bayes

Law of Probability

  • Dependent and Independent Events
  • Bayes Theorem
  • Formula for Posterior Probability

Naive Bayes Classifier

  • Characteristics of a Naive Bayes Classifier
  • The “Naive” Assumptions
  • Customer Churn Example

Practical and Performance Considerations

  • The Case for Smoothing
  • Laplace (Add-One)
  • Thinking about Training vs Prediction Speed

Naive Bayes in Action

  • Spam Classification
  • Predicting on Text (Corpus)
  • Predicting Political Party Affiliation

Module 2: Tree-Based Methods and Ensembles

Decision Trees

  • Advantages and Model Characteristics
  • Information Gain and Splitting Criterion
  • Pruning and Tree Size

Decision Trees in Action

  • Predicting Diabetes from Diagnostics Measurement
  • AUC Curve
  • Key Considerations and Practical Advice

Machine Learning Theories

  • Logistic Regression, Naive Bayes, and Decision Trees Have More in Common Than You Think
  • Industrial Applications
  • Thinking About Decision Boundaries

High-Performance Machine Learning

  • Bias-Variance Tradeoff Revisited k-Fold Cross-Validation
  • Predicting Exercise Form with Fitness Tracker Data

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Identifying Risky Bank Loans

  • Use any of the 3 classification algorithms you’ve learned in this lesson to predict the risk status of a bank loan. The variable default in the dataset indicates whether the applicant did default on the loan issued by the bank.

    Use an R Markdown document to lay out your process, and explain the methodology in 1 or 2 brief paragraph. The student should be awarded the full (3) points when:

    • The preprocessing steps are done, and the student show an understanding of holding out a test/cross-validation set for an estimate of the model’s performance on unseen data
    • The model’s performance is sufficiently explained (accuracy may not be the most helpful metric here! Recall about what you’ve learned regarding specificity and sensitivity)
    • The student demonstrated extra effort in evaluating his/her model and proposes ways to improve the accuracy obtained from the initial model

Ad-Hoc Course Registration:

  • Date: 18 – 21 January 2021
  • Time: 18.30 – 21.30
  • Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
  • Investment: Rp. 5.200.000
  • Date: 18 – 21 January 2021
  • Time: 18.30 – 21.30
  • Investment: Rp. 2.600.000


Workshop Receivables:

  • Workshop Lecturer’s Notes

    Including 2x Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout the 4-day course.

  • Certification of Completion

    Show current and prospective employers that you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, small group size. Dinners are included for evening workshops.

  • Supplement Materials

    Receive supplement datasets to practice on, reference notes, working files (R Notebook or Jupyter Notebook), and other materials that will help you master the topics.

This workshop is recommended for:

The Machine Learning: Classification 2 workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.

Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.

Past Workshops in this Series:

Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.

Part of the Machine Learning Specialization

This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.