DATA SCIENCE FUNDAMENTALS : Machine Learning

HUBBA-TO, Bangkok
25 - 27 March 2019
6.30 - 9.30pm (GMT +7)
50 participants
Difficulty: Beginner
Regular ticket : 8.000 Baht
Delivered in English

Testimonial from Previous Workshop :

Workshop Receivables:

Workshop Lecturer’s Notes
Including Course Book (PDF), HTML files, course transcripts (if any).
Highly-accelerated Learning
Learn under the assistance of our lead instructor and a band of qualified teaching assistants throughout the 3 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.

Course details:

This workshop is a careful combination of statistical theory, hands-on coding and programming exercises to help students understand — and implement — some of the most widely used, and fundamental, machine learning algorithms.

By building regressors and classifier algorithms from scratch, the student will go beyond applying machine learning models to actually developing their own models — and learn the right approach to fine-tuning the model performance as well as evaluating model fit against unseen data. Upon completion of the workshop, the student will be well versed in an array of important, versatile machine learning algorithms and equipped with the right knowledge to apply them to future datasets in their daily job.

Difficulty: Beginner
Pre-requisites: None
3-Day Workshop
9 hours

Click here for more information about the event.

Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! ~ John Elder, Elder Research

Please bring along:

1x Laptop
1x Purchased ticket

SYLLABUS:

Introduction to Data Science

Description of course materials and the learning environment
A comprehensive view on the roles of data science, the relating professions, career prospects and outlook
Description of the workflow, tools, setup and programming languages in the course

R Programming Basics

Setting up the Workspace and Environment
Working with data types: scalar, vector, list, matrix, data frame
R’s built-in functions for data manipulation
Inspecting data and data wrangling basics
R’s plotting capabilities
R Markdown and reproducible research

Statistical Fundamentals (Machine Learning focused)

Demonstrate the use of various statistics in exploratory data analysis: 5-number summary, mean, mode, interquartile range, variance, standard deviation and correlation
Confidence intervals and hypothesis testing
p-value and statistical applications in machine learning

Data Wrangling

Continuous variables and Categorical variables
Factors and levels
Description of the workflow, tools, setup and programming languages in the course
Reading from different data formats: CSVs, JSON, webpages, API
Various data preprocessing and data cleansing techniques

Introduction to Linear Regression

Machine learning challenges: Regression vs Classification
Linear Regression models
Model construction and prediction in R
Mathematical details of a simple linear regression model: slope, intercept, coefficients, p-value, R-squared, residuals

Multiple Regression

Closer look at predictors (independent variables)
Adjusted R-squared
Multicollinearity
Assumptions check
Occam’s Razor

Model Evaluation

Evaluating a regression model
Evaluating a classifier
Bias-variance tradeoff
Machine learning in practice

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Course Producer

Samuel Chan

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), a certified professional (certificates from Microsoft, MongoDB, Stanford University, John Hopkins University), and an experienced consultant that has worked with several public-trading companies from his time staying in China, Japan and Singapore.

Between 2017 and 2018, Samuel has trained and consulted with more than 20 companies around Indonesia and a regular guest speaker/trainer in a number of universities in Singapore and Indonesia. He 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.