Machine Learning Models with Financial Applications (Experience Course)
22 Jun 2019 09:30am - 01:30pm
Event ended
Machine learning models with    financial applications

Machine Learning Models with Financial Applications (Experience Course)

22 Jun 2019 09:30am - 01:30pm

More Events R One 26F Sino Plaza 255-257 Gloucester Road, Causeway Bay, Hong Kong
Registration closed

 

 

 

 

Introduction

 


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.

 


Overview

 


The following three questions would summarize what you are able to learn in just EIGHT hours:

 


1. How to plot AAStock-Style of Charts on daily HK/US Stock Prices, FX and Precious Metal Prices in 3 seconds?

2. How to make Stock Buy/Sell Decisions with Machine Learning Techniques?

3. How to make a computer to understand the Textual Customer Feedback on the website for a restaurant owner?

 


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.

 

 

What You Will Learn:

 

 

Unit 1:

 


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?

 

 

Benefits & Outcome

 


On completion of this course, you will get the following changes:

1. You will have a better understanding of concepts of AI and Machine Learning.

2. You will have acquired the knowledge and skills of Data Science to surpasss your peers

3. You will have grasped the basic data manipulation techniques that facilitate you to predict security prices more efficiently.

4. You will have confidence to learn more Machine Learning models as you will find it interesting and not-so-difficult as you may have expected.

5. You will have a better understanding of your potential capabilities to ride on the Big Data waves

 

 

Expected Participants: Data analysts, programmers, executives, university undergraduate students and those who are interested in AI techniques and Big Data analysis.

 

 

 


Language: Cantonese (All electronic course materials are prepared in English)

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