Machine Learning Certification Course in Sydney, Australia

• Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
• Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
• Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
• Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
• Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
• Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

• 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

Simplilearn’s Machine Learning course is hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms. 

Project 1: Fare Prediction for Uber

Domain: Delivery (Commerce)
Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.

Project 2: Test bench time reduction for Mercedes-Benz

Domain: Automobile

Mercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimise the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective. 

Project 3: Income qualification prediction for Inter-American Development bank

Many social programs have a hard time making sure the right people are given enough aid.
It’s tricky when a program focuses on the poorest segment of the population. This segment of
population can’t provide the necessary income and expense records to prove that they qualify.
Predicting the right set of people to be included for the aid remains a big challenge for  Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using random forest classifier.

Project 4: Access privileges prediction for Amazon.com employees

There is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their
provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.

Participants in this Machine Learning online course should have:

• Familiarity with the fundamentals of Python programming 
• Fair understanding of the basics of statistics and mathematics