Duration:3 Days A$4995

(Ex-GST and Includes Exam)

Program Overview

Machine learning is a subfield of computer science, often referred to as predictive analytics, or predictive modeling. Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.

Machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. These algorithms are heavily based on statistics and mathematical optimization.

Optimization is the process of finding the smallest or largest value (minima or maxima) of a function, often referred to as a loss, or cost function in the minimization case. One of the most popular optimization algorithms used in machine learning is called gradient descent, and another is known as the the normal equation.

In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques.

Machine learning isn’t one singular tool, it’s an entire field of tools – each with their own strengths and weaknesses.

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What will you learn?

By the end of this Machine Learning course, you will be able to accomplish the following: 

  • Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
  • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
  • Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems


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Who Should Attend

  • Developers aspiring to be a data scientist or machine learning engineer
  • Analytics managers who are leading a team of analysts
  • Business analysts who want to understand data science techniques
  • Information architects who want to gain expertise in machine learning algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in data science and machine learning
  • Experienced professionals who would like to harness machine learning in their fields to get more insights