R One, 26F Sino Plaza 255-257 Gloucester Road, Causeway Bay
This is an introductory course that lets you have a taste of AI, Machine Learning and R Language. You will get an invaluable experience to understand Machine Learning Models and their business applications. Consider taking the first step to get yourself prepared for the imminent Big Data era.
Machine learning is a subset of AI and Data Science. It is an algorithmic approach of data mining and predictive modeling that enables software applications to become more accurate in predicting outcomes without being explicitly programmed. The essence is to ask your computer to self-learn massive data to look for hidden patterns. The Machine Learning approach of predictive data analysis is changing the traditional way how a company gets business insight from the company data.
The following three questions would summarize what you are able to learn in just EIGHT hours:
This is an introductory course that will equip you with the basic R Programming skills to get you familiar with selected topics of popular machine learning models. It covers the skills of data cleansing and concepts of Machine Learning, a number of Regression Models, Decision Trees and Random Forest Classifiers.
We will use real-life business cases to illustrate and apply what you will have learnt. You will find programming solutions to all of the above three questions and you will practically "do it yourself" with R with confidence.
Introduction to R Programming for Data Preparation: • Data Import • Data Cleansing • Data Recoding • Standardizing Variables
Financial Analysis with R • Programming Approach to Collect Daily Financial Data (HK/US Stock prices, FX, Precious Metal prices) • Building Financial Data Charts with AASTOCK styles (Moving Average, Bollinger Band, RSI, MACD, etc.) Case Study: How to plot AAStock-Style of Chart of Stock Prices (HK/US), FX and Precious Metal Prices in 3 seconds?
Unit 2: Concepts of Machine Learning: Supervised, Un-supervised and Reinforcement Learning Model Assessment Techniques: Cross Validation, Confusion Matrix, RMSE and AIC.
Machine Learning Models • Multiple Linear Regression • Logistic Regression • Multinomial Regression Case Study: How to make Buy/Sell Decisions on Stock with Machine Learning Techniques?
Unit 3: Decision Trees and Random Forest Case Study: How to make a computer to understand the Textual Customer Feedback collected from a restaurant website?
On completion of this course, you will get the following changes:
Data analysts, programmers, executives, university undergraduate students and those who are interested in AI techniques and Big Data analysis.
Cantonese (All electronic course materials are prepared in English)