Professional Certificate of Competency in Practical Machine Learning Using Python for Engineers and Technicians

Integrated Masters with deeper specialization and research focus
Taught in English
Duration 3 Months
Mode Online
Level Undergraduate
Degree Undergraduate Certificate
Closing soon June 2027

Course Overview

The Professional Certificate of Competency in Practical Machine Learning Using Python for Engineers and Technicians from the Engineering Institute of Technology is designed for engineers and technicians aiming to integrate advanced machine learning techniques into their work. This online, part-time certificate equips you with the essential skills to apply supervised and unsupervised learning methodologies to solve complex engineering problems. You will gain a solid understanding of foundational concepts, including basic machine learning terminology, linear algebra with Python, and probability theory, preparing you for practical application in real-world scenarios.

Over three months, you will explore 12 comprehensive modules, delving into topics such as feature engineering, classification, regression, and neural networks, all implemented using Python. The course structure emphasizes hands-on application, covering practical aspects like building a machine learning system and deploying models using Flask for web integration. You will develop proficiency in libraries like NumPy and Pandas, and gain experience with techniques such as K-means clustering and decision trees. Upon completion, you will be ready to leverage machine learning to enhance problem-solving capabilities in your engineering practice.

Course Curriculum & Details

Detailed information for serious evaluators

Program Overview

This professional certificate program equips engineers and technicians with practical machine learning skills using Python. You will explore fundamental concepts including supervised, unsupervised, and reinforcement learning, alongside essential mathematical foundations in linear algebra and probability theory. The curriculum covers feature engineering, various machine learning algorithms like clustering and regression, and delves into neural networks, including convolutional and recurrent architectures. Finally, you will gain experience in natural language processing, practical applications, and web deployment of machine learning models.

1 Module 1: Basic Machine Learning Terminology

Machine Learning and Artificial Intelligence
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Building a Machine Learning System
Evaluating a Machine Learning System

2 Module 2: Linear Algebra with Python using Numpy and Pandas

Linear Algebra Review
Introduction to Anaconda
Introduction to Pandas
Introduction to Numpy
Linear Algebra using Numpy

3 Module 3: Probability Theory and Statistics with Python using Numpy and Pandas

Data Plotting in Python
Statistics
Probability and Random Variables
Useful Probability Distributions
Derivatives

4 Module 4: Feature Engineering

Data Loading and Manipulation using Pandas and Numpy
Working on Images
Features and Feature Vectors
One-hot Encoding
Feature Normalization

5 Module 5: Unsupervised Learning

Clustering using K-means Algorithm
K-Means Implementation
Clustering using Expectation-Maximization
Association Rules and Recommender Systems

6 Module 6: Supervised Learning

Classification
Regression

7 Module 7: Feedforward Neural Networks

Mathematical Neural Models
The Perceptron
The Gradient Descent Algorithm
Multi-Layer Perceptron

8 MODULE 8: Convolutional and Recurrent Neural Networks

Deep Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks

9 MODULE 9: Natural Language Processing – Part I

Problems Solved by Natural Language Processing
Text Preprocessing
Regular Expressions
Discrete Features

10 MODULE 10: Natural Language Processing – Part II

Word Embeddings
Part of Speech Tagging
Text Classification using Naïve Bayes
Text Classification using Neural Networks

11 MODULE 11: Practical Applications

Industrial Knowledge Representation using Decision Trees
Industrial Fault Diagnosis using Feedforward Neural Networks
Sound Classification using Feedforward Neural Networks
Image Classification using Convolutional Neural Networks
Machine Translation and Chatbots using Recurrent Neural Networks

12 MODULE 12: Web Deployment

Use of Flask
Integrating machine learning models with Flask
Deploying applications to a Web Server

Cost & Affordability

A clear picture of what this course will cost you

Program Costs

First Year Fee Tuition fee
USD 979.00
Total Program Cost Estimated total tuition
USD 979.00

Currency note: Program costs shown in USD based on the available tuition breakdown.

Living Costs

Accommodation
USD 580.00 - USD 1,974.00
Food & Groceries USD 2,122.00 - USD 2,971.00
Transport USD 849.00 - USD 1,698.00
Utilities & Bills USD 849.00 - USD 1,698.00
Estimated Total USD 3,820.00 - USD 6,367.00

Living cost estimates are based on Perth Campus.

Outcomes & Employability

What happens after you graduate

79%
Graduate Employment Rate Within 6 to 12 months of graduation

Common Graduate Roles

Blockchain & Emerging TechnologiesCloud Computing & InfrastructureCybersecurityData Science & AnalyticsDevOps & Site Reliability EngineeringHardware & Semiconductor EngineeringIT Support & Systems AdministrationNetworking & Internet ServicesProduct Management & UXQuality Assurance & Testing

Top Employers Hiring for this course

Cognetiks Consulting

Why Study Professional Certificate of Competency in Practical Machine Learning Using Python for Engineers and Technicians at Engineering Institute of Technology

What makes this program stand out

June 2027 Closing soon

Next intake is in June 2027

Life at Engineering Institute of Technology in Australia

What daily life looks like for international students

Language

English

Primary language everywhere

Climate

Mild & Rainy

10-20°C average, pack layers

Safety (Numbeo Safety Index)

Very Safe

Exeter is one of UK's safest cities

Frequently Asked Questions

Common questions about Professional Certificate of Competency in Practical Machine Learning Using Python for Engineers and Technicians at Engineering Institute of Technology

Can I work while studying?

International students can work up to 48 hours per fortnight during term time and unlimited hours during breaks. No separate work permit is necessary.

What English language score do I need for this course?

To enroll, students must meet IELTS requirements: a listening score of 5.0, overall score of 5.5, and 5.0 in reading, speaking, and writing. Ensure your scores meet these criteria to proceed.

What jobs can I get after studying this course?

Data Scientist, Machine Learning Engineer, AI Technician, Python Developer, Engineering Analyst.

What will I actually study in this course?

This professional development course is designed for engineers and technicians who need to harness machine learning technologies in their engineering work or become better problem solvers through the application of machine learning. This course will teach you to use Python Programming to work with machine learning applications and to apply supervised and unsupervised machine learning to engineering problems.

Course Benefits

  • Receive a Certificate of

When can I start — what are the intake months?

The next intake for the course is in June. Make sure to apply before the deadline to secure your spot.
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