$250 – $350

Machine Learning: A Primer for Aspiring Data Scientists

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101 Collins Street

Level 18

MELBOURNE, VIC

Australia

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Refunds up to 1 day before event

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Description

This unit provides you with an introduction to Machine Learning. The course is combination of theory and practical aspect of machine learning that gives you not only a good understanding of main machine learning concepts but also teaches you how to apply these concepts to solve your domain specific real world applications.

The course will discuss fundamental problems such as classification, regression, feature engineering, model selection, clustering, dimensionality reduction, recommender systems, etc.


A welcome pack consisting of:

  1. Printouts of the slides,
  2. USB stick with Python and Java code used during the tutorial (Recommended for Linux and mac computers),
  3. Certificate of Attendence,

will be provided.


Tea/Coffee provided.


Course outline is as follows, * means a practical component:

Module 1 - Introduction to Machine Learning

  • - What is machine learning, and why you need to know about it?
  • - Machine learning applications in our daily lives.
  • - Introduction to Data Science and Big Data.
  • - Introduction to different data types.
  • - World of SQL and NoSQL.
  • - Statistics 101

Module 2 - Fundamental Problems in Machine Learning

  • Classification/Regression.
  • Linear Regression, Logistic Regression, Naive Bayes.
  • Generative vs. Discriminative Learning.
  • * Writing your first machine learning model in python/java.

Module 3 - Model Selection

  • - Introduction to bias and variance
  • - Regularization
  • - Feature Engineering
  • - Concept of Kernel and Kernel trick
  • - Training/Testing Splits, concept of cross-validation
  • - Designing a Machine learning experimentation platform
  • * Writing your first experimentation engine

- Discussions over lunch

Module 4 - Introduction to Unsupervised Learning

  • - K-means, DB-Scan, EM algorithm
  • - Overview of anomaly detection
  • * Writing your first clustering program and applying on sample datasets
  • - Association rules and discovery
  • - Dimensionality Reduction

Module 5 - Introduction to Recommender Systems

  • - Overview of Netflix challenge
  • - Content-based Recommendations
  • - Collaborative Filtering
  • * Writing your first recommender systems
  • - Evaluating recommender systems

Module 6 - Introduction to Advanced Concepts

  • - Ensemble Learners
  • - Boosting, Bagging, Stacking
  • - Random Forests and Gradient Boosting
  • - Deep Learning
  • - Big Data and Big Models

Target Audience:

- You are an under-grad/post-grad uni student studying towards your degree with a data analytics component

- You are working in a data science or related role or you are shifting your focus towards mahcine learning and analytics


What to Bring:

- Laptop (optional)

- Writing pad and pen for taking notes

What we expect from you:

- Get involve in discussions

- Share your past experience

- Mingle and make connections with other attendees.

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Date and Time

Location

101 Collins Street

Level 18

MELBOURNE, VIC

Australia

View Map

Refund Policy

Refunds up to 1 day before event

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