- Course at a Glance
- Code: CMP
- Course Length: 3 Months
This Three-Month Program is Delivered via EIT's Innovative LIVE ONLINE Approach.
Machine learning is undoubtedly one of the most exciting technologies in recent times. Both large and small companies are embracing it with tremendous results. For example, if you are working with a large amount of sensor data and you want to predict an output value for given inputs, or you want to find clusters in your sensor data, you can use Machine Learning (ML) algorithms. Python is the number one language of choice when working with machine learning applications as it is powerful, yet simple, and accessible. Upon completion of this program, you will:
- Boost your engineering career with 21st Century Machine Learning skills
- Learn how to use Python to solve engineering problems
- Learn how to apply supervised and unsupervised Machine Learning to engineering problems
- Learn how to apply Deep Learning
- Develop strategies in interpreting Machine Learning output
- Create graphical plots and schematics
If you are keen to harness machine learning technologies in your engineering work, or indeed become a better problem solver or perhaps consider a career in machine learning, then this program is for you. There are many programs out there, but this one focuses on engineering and industrial applications.
The best way to learn the technologies is to work through practical examples of machine learning in a systematic way. Two types of machine learning will be tackled in this program:
Supervised learning: This is where we learn the relationship of given inputs to a set of outputs. For example, how different sensor inputs for a process plant can predict the likelihood of a breakdown of a pump or requirement for maintenance on an item of equipment. You already know how to classify the earlier input data to previous breakdowns of the pump; so, you want to use new input data to predict this event for you so that you can act before it actually happens. Algorithms that you will learn about here include linear regression, logistic regression, discriminant analysis, decision trees, Naïve Bayes, support vector machines, and random forests.
Unsupervised learning: This occurs when there is no pre-defined relationship between input data and an output variable. An example here would be to take sensor data from hundreds of similar industrial plants, then asking the algorithm to find patterns and classify the data. You do not know how to classify the data but want your algorithm to find any patterns and to do the classification of the data for you. Algorithms that you will learn about here include K-means clustering and Gaussian Mixture models.
Although there are no formal entrance requirements, students are required to have a basic understanding of Python programming.
Who Will Benefit
- Engineering technicians and technologists working in electrical, mechanical, civil or industrial automation fields
- Programmers looking to upskill in Python
- Engineering supervisors
- Project Managers
- Anyone wanting to learn machine learning programming from an engineering perspective
Duration and Time Commitment
There is a considerable amount of useful practical material to cover in this three-month course. To ensure you get the maximum value from the course, we provide highly interactive webinar sessions where the instructor covers the key elements of the course in a web conferencing format. These live webinars last for approximately 90 minutes, including class discussions. Successful students are likely to spend between 5-8 hours per week getting to know the course content. This includes attending the fortnightly webinars.
Participants who achieve at least 60% in each assignment and complete all homework, plus attend 65% of the live webinars will receive the EIT Certificate of Competency in Practical Machine Learning using Python for Engineers and Technicians.
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
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
MODULE 3: Probability Theory and Statistics with Python using Numpy and Pandas
- Data Plotting in Python
- Probability and Random Variables
- Useful Probability Distributions
MODULE 4: Feature Engineering
- Data Loading and Manipulation using Pandas and Numpy
- Working on Images
- Features and Feature Vectors
- One-hot Encoding
- Feature Normalization
MODULE 5: Unsupervised Learning
- Clustering using K-means Algorithm
- K-Means Implementation
- Clustering using Expectation-Maximization
- Association Rules and Recommender Systems
MODULE 6: Supervised Learning
- K-Nearest Neighbors Algorithm
- Gaussian Mixture Models
- Decision Trees
- Local Mean
- Regression Trees
- Linear Regression
MODULE 7: Feedforward Neural Networks
- Mathematical Neural Models
- The Perceptron
- The Gradient Descent Algorithm
- Multi-Layer Perceptron
MODULE 8: Convolutional and Recurrent Neural Networks
- Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
MODULE 9: Natural Language Processing - Part I
- Problems Solved by Natural Language Processing
- Text Preprocessing
- Regular Expressions
- Discrete Features
MODULE 10: Natural Language Processing - Part II
- Word Embeddings
- Part of Speech Tagging
- Text Classification using Naïve Bayes
- Text Classification using Neural Networks
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
MODULE 12: Web Deployment
- Use of Flask
- Integrating machine learning models with Flask
- Deploying applications to a Web Server
Instructor – Hadi Harb
Instructor – Hadi Harb
Hadi Harb, MEng, MSc, PhD, has more than 20 years of experience in the development and management of Artificial Intelligence and Audio Signal Processing projects.
From 2000 to 2004 he pursued his PhD working on content-based multimedia indexing. He then worked as a research engineer at Centrale Lyon Innovation SA from 2004 to 2006. During his PhD and research engineer work period, he participated in many R&D projects in collaboration with world-class institutions such as INRIA, France Télécom R&D and IRCAM. He issued for 2 patents and published 17 articles in known international scientific journals and conference proceedings.
From 2006 to 2015 he founded and managed Ghanni, a company specialized in multimedia content recommendation and identification. Several European radio stations and websites licensed Ghanni’s music recommendation technology. In 2015 he restructured Ghanni to transform it into a consultancy company in the domain of Artificial Intelligence where he acts as the principle consultant. His current interests are in the use of Artificial Intelligence techniques to solve industrial problems.
Hadi holds a MEng (2000) in electrical-electronic engineering. He earned his MSc in 2001 and PhD in 2004 both in computer science from the Institut National des Sciences Appliquées INSA Lyon, and the Ecole Centrale de Lyon respectively.
EIT programs are specifically designed by an international body of industry experts, ensuring you gain cutting-edge skills that are valued by employers around the world.
Industry Experienced Instructors
Our instructors include highly experienced engineers with real-world knowledge, not just academics.
Our innovative online delivery model ensures that you have access to the best instructors and resources 24 hours a day. You can participate from anywhere in the world, as long as you have an internet connection.
You will be supported by a dedicated learning support officer for the duration of your studies, giving you a greater chance of success.
EIT’s current students join from over 140 countries, with expert instructors and tutors based around the globe, providing you with a truly international perspective.
EIT is one of the only private colleges in the world specializing in engineering.
Online Delivery Mode
EIT recognizes that many of our potential students have work and/or family commitments which makes pursuing further study very challenging. Our online programs have been specifically designed to reduce the significant financial, time and travel commitments often required by traditional on-campus programs. Benefits of online delivery include:
- Upgrade your skills and refresh your knowledge without having to take valuable time away from work
- Learn from almost anywhere – all you need is an Internet connection
- Interact and network with participants from around the globe and gain valuable insight into international practice
- Learn from international industry experts
- Revisit recordings of webinars whenever and as often as you wish
EIT uses an innovative, online approach to ensure that you have a supportive, interactive and practical education experience. Our delivery model involves live, interactive online webinars, practical sandbox environment and hands-on weekly problem solving exercises with support from a dedicated Learning Support Officer and academic staff.
In addition we provide additional resources and reading guides, which you examine at your convenience.
Webinars are conducted using a specialized, live, interactive software system. You will receive course materials and assessments through an online student portal which is available 24 hours a day.
All you need to participate is an adequate Internet connection, a computer, speakers and, if possible, a microphone. The software package and setup details will be sent to you prior to the program.
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