Machine Learning Using Python | SGInnovate



Machine Learning Using Python

Presented by General Assembly
Partnered with SGInnovate

This two-day workshop will introduce students to data exploration and machine learning techniques. Students will learn about the data science workflow and explore data visualisation using Python and built-in libraries. Students will also learn the differences between supervised and unsupervised learning techniques and practise creating predictive regression models.

Note: This is a two-day workshop and the second session will be on Saturday, 11 May 2019.

In this course, participants will:

  • Collect data from a variety of sources (e.g., Excel, web-scraping, APIs and others)
  • Explore large datasets
  • Clean and "munge" the data to prepare it for analysis
  • Apply machine learning algorithms to gain insight from the data
  • Visualise the results of your analysis
  • Build your own library and Python scripts

Recommended Prerequisites:

  • A background in computer science, programming, and/or statistics is preferred for this workshop
  • It is not required but it would be helpful if you are familiar with the command line tools and how to write simple programs
  • It is recommended that you take the “Python for Beginners” workshop prior to attending this workshop

Day 1

Developing the Fundamentals
Module 1: Introduction to machine learning 

  • What is machine learning?
  • Installation and update of tools
  • Machine learning algorithms

Module 2: Exploring and using datasets 

  • Learn the steps to pre-process a dataset and prepare it for machine learning algorithms

Day 2

Diving into machine learning
Module 3: Supervised vs. unsupervised learning

  • Review of machine learning algorithms
  • Classification, linear regression and logistic regression
  • Random forests, clustering
  • Decision trees

Module 4: Model Evaluation

  • Feature Engineering and Model Selection
  • Model Evaluation Metrics - Accuracy, RMSE, ROC, AUC, Confusion Matrix, Precision, Recall, F1 Score
  • Overfitting and Bias-Variance trade-off
  • Cross-Validation
  • Preparation

Gaurav Chaturvedi, Senior Vice President, DBS Bank

Gaurav is the Lead Data Scientist at DBS Bank.

He lends simplicity to the (sometimes very mathematical) concepts in Data Science. He can easily relate the concepts of data science to practical problem-solving situations you are likely to find yourself in. His style is well suited for beginners as well as experienced programmers.

Gaurav enjoys following and writing about cricket in his free time.

Topics: Artificial Intelligence / Deep Learning / Machine Learning / Robotics