Unsupervised Machine Learning

Discovering Hidden Structures in Data

R, Machine Learning
Pre-Requisite:
- Machine Learning Classification 1
Difficulty: Intermediate+
Associated Specializations:
- Machine Learning
Duration: 4 Days

Ad-Hoc Course Registration:

ON-SITE CLASS

Date: 25 – 28 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: 25 – 28 January 2021
Time: 18.30 – 21.30
Investment: Rp. 2.600.000

Course details :

Learn PCA (Principal Component Analysis), Clustering, and other algorithms to work with unsupervised machine learning tasks where the target variable is not known or defined. Applying what you’ll learn from this workshop, you will be tasked to develop an anomaly detection or an e-commerce product recommendation model that can be related to real-life business scenarios.

We strongly recommend that you complete the pre-requisite courses prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who are new to the field of machine learning.

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Schedule

Background
Day 1
Principal Component Analysis
Day 1
PCA from First Principles
Day 1
PCA in Action
Day 2
PCA in Action II
Day 2
Understanding Clustering
Day 2
k-Means Clustering in Action
Day 3
Evaluating k-Means
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: Unsupervised Machine Learning

Module 1: Dimensionality Reduction

Background

Understanding Unsupervised Learning
The “Dimensionality” Problem
Industrial Use of PCA

Principal Component Analysis

Rethinking About Covariances
The Case for PCA
Eigenvalues and Eigenvectors

PCA from First Principles

Just Enough Matrix Algebra
Mathematical Proof
Visualization and Visual Proof

PCA in Action

Dubious Property Sales in NYC
PCA on US Arrests Data
Biplot and The Variables Factor Map

PCA in Action II

Eigenfaces
PCA on Credit Loan Data
Deconstruction and Reconstructing Faces with PCA
Principal Components by Hand

Module 2: k-Means Clustering

Understanding Clustering

Centroid-based Clustering Algorithms
The k-Means Procedure
Mathematical Details

k-Means Clustering in Action

Cluster-based Product Recommendation
Scaling and Implementation Details
Visualizing Clusters

Evaluating k-Means

Between Sum-of-squares
Within Sum-of-squares
Combining k-Means with PCA

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Diving into Wholesale Transactions

Using any of the two unsupervised learning algorithms you’ve learned, produce a simple R markdown document where you demonstrate an exercise of either clustering or dimensionality reduction on the wholesale.csv data provided to you

Digging Deep into NYC Property Sales

Using any of the two unsupervised learning algorithms you’ve learned, produce a simple R markdown document where you demonstrate an exercise of either clustering or dimensionality reduction on the nyc data provided to you

Explain your choice of parameters (how you choose k for k-means clustering, or how you choose to retain n number of dimensions for PCA) from the original data. What are some business utilities for the unsupervised model you’ve developed? The R Markdown document should be no longer than 4 paragraph and contain one or two visualizations.

Ad-Hoc Course Registration:

ON-SITE CLASS

Date: 25 – 28 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: 25 – 28 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 Unsupervised Machine Learning 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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Unsupervised Machine Learning

Ad-Hoc Course Registration:

Course details :

Schedule

Course Producer

Samuel Chan

4-Day Workshop Modules

Module 1: Dimensionality Reduction

Background

Principal Component Analysis

PCA from First Principles

PCA in Action

PCA in Action II

Module 2: k-Means Clustering

Understanding Clustering

k-Means Clustering in Action

Evaluating k-Means

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