Machine Learning with Python - Introductory Course Series (3rd run)

From:4 Apr 2019 - 18:30
To:9 May 2019 - 20:30
Location:AXA Tower (Rooms Outram-Marymount Level 26), 8 Shenton Way, Singapore 068811


TWO seats left!

 

Dear SAS members

 

Following the successful first two run of the “Machine Learning with Python – Introductory Course Series”,  the Data Analytics committee is pleased to launch a 3rd run of the same series taking place all Thursday evenings of April-May.

This 3rd series of course is limited to 20 people who are highly motivated to complete the course and every homework that will be assigned each week. See what you can expect in this post  ; the material and courses will remain similar (some enhancements).

 

Details:

5 sessions Thursday from 6:30- 8:30pm. F&B provided.

The dates are as follows: 4, 11, 25 April & 2, 9 May 2019

Venue: Rooms Outram-Marymount, AXA, Level 26, Axa Tower 8 Shenton WaySingapore 068811

 

 

6:30pm  Registration, Computer set up, F&B

7:00pm  Course commences

8:30pm  Course ends

9:30pm  Venue closes

 

An email (with IT set up on your own laptop to bring at the sessions) will be sent to all registrants when payment has been made.

We expect similarly to the participants of the two first series that all will complete the course and will then receive a SAS 'certificate of attendance’. CPD hours is 10.

 

 

Objectives:

Data Scientists are one of the most sought after jobs in the world today and is ranked as the number 1 job on Glassdoor. Although considered the ‘sexiest’ job of the 21st century, the Data Scientist has many similar skill-sets and responsibilities as an Actuary. This course aims to equip the modern-day Actuary with foundational to intermediate Python programming skills with an emphasis on Machine Learning. This will enable Actuaries to navigate the fast changing face of the current data landscape and catch up on the Artificial Intelligence revolution. This course aims to:

- Expose SAS members to a Machine Learning using the Python Programming Language.

- Enable SAS members to use Python to perform day-to-day Actuarial functions

- Provide an alternative tool to overcome the limitations of conventional tools like Microsoft Excel.

- Guide SAS members to apply their learnings of machine learning and predictive modelling by participating in a Kaggle competition

 

Topics:

- Data science methodology

- Introduction to Python (Language/Coding/Libraries)

- Data manipulation and Visualization

- Exploratory data analysis

- Breakdown of industry-grade Machine Learning algorithms

- Evaluation strategies

- Practical case study (predict Titanic survivors) with detailed exploration of different machine learning techniques

 

Teacher:

Karthikan is a Data Scientist at Aviva, with a strong Data and Strategy consulting background. He employs advanced analytics tools extensively to solve business problems and deliver insights that moves the needle for Aviva across various regions and lines of businesses. Having an academic background in Actuarial Science, Karthikan has experience in the Actuarial practice and contributes as a member of the Singapore Actuarial Society Data Analytics Committee to further develop the analytics skill sets of the actuarial practice.

 

 

Cost: 300 SGD for paid up Members (please login with your Username before signing-up)

This second series of course is not opened to non-members and student members

S$300 for payment via cheque / internet banking / cash deposit at any DBS branches is preferred.

S$312.75 for payment via Paypal.  During Paypal payment, please wait until the transaction is complete and you are transferred back to the SAS website before closing your browser.

 

If you would like to attend, please register by signing up by 22nd March through this link:  Machine Learning with Python INTRODUCTORY COURSE SERIES III   <<< Online Registration Link

 

Additional information required for SAS member registrants   <<< click here to complete

 

Thank you

 

Frederic Boulliung

Data Analytics Committee

Singapore Actuarial Society






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