Machine Learning: Classification 2

The science of solving classification tasks

R, Machine Learning
Pre-Requisite:
- Regression Models
Difficulty: Intermediate+
Associated Specializations:
- Machine Learning
Duration: 4 Days

Ad-Hoc Course Registration:

ON-SITE CLASS

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

ONLINE CLASS

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.

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Schedule

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:

ON-SITE CLASS

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

ONLINE CLASS

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.

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WhatsApp: 0816-692-471
Email: community@algorit.ma

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Machine Learning: Classification 2

Ad-Hoc Course Registration:

Course details :

Schedule

Course Producer

Samuel Chan

4-Day Workshop Modules

Module 1: Naive Bayes

Law of Probability

Naive Bayes Classifier

Practical and Performance Considerations

Naive Bayes in Action

Module 2: Tree-Based Methods and Ensembles

Decision Trees

Decision Trees in Action

Machine Learning Theories

High-Performance Machine Learning

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Ad-Hoc Course Registration:

Workshop Receivables:

Workshop Lecturer’s Notes

Highly-accelerated Learning

Certification of Completion

Quality Learning Environment

Supplement Materials

This workshop is recommended for:

Past Workshops in this Series:

Hands-on Coding

Working through real-life examples

Demo and Presentation

Learn-by-building philosophy

Part of the Machine Learning Specialization

Get In Touch

Data Science School

Company