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Formats:
Live Online
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Duration:
30 hours
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Registration Fee:
$890
Start Date
Machine Learning Course- Overview
This course will explore the concepts of machine learning and help students understand how it is transforming the digital world. Students will recognize how to enable computers to learn and adapt through experience to do specific tasks without explicit programming.
Machine learning is the method by which artificial intelligence (AI) can automatically learn and adapt to new information without being manually programmed. Machine learning is becoming more commonplace as AI advances and businesses are looking to make the most out of this technology.
Our machine learning course breaks down various machine learning models and shows students how these forms of AI can be used to provide unique business solutions. Throughout the machine learning crash course, students will learn the differences between supervised and unsupervised learning and find out how recommendation engines and time-series modelling works.
Many AI programs run through cloud services, and our machine learning online course will demonstrate what services are available and what functions they best serve. Students will leave the machine learning course with a firm understanding of the fundamentals of how AI learns and interacts in a digital space.
Course Prerequisites
None. However, knowledge of logic and any programming language is an asset.
Topics Covered/Learning Objectives
Upon completion of this course, the successful student will have reliably demonstrated the ability to:
- Understand the concepts of machine learning and know its history and applications
- Acknowledge the trade-off between prediction accuracy and model interpretability
- Be familiar with the differences among various ML algorithms
- Master several supervised regression and classification methods, including support vector machines
- Perform unsupervised learning and similarity learning
- Understand ensemble learning for tree-based methods
- Know time series analysis or text mining
- Validate models through cross-validation
- Choose and justify the appropriate ML approach for a given business problem
- Understand how machine learning works to bring a technical perspective to the workplace
| Program Organization | # of Hours |
| Lesson 1: Introduction to AI, ML and DL* | 3 |
| Lesson 2: Data Preparation and Preprocessing | 3 |
| Lesson 3: Supervised Regression (Linear/Logistic Regression) | 3 |
| Lesson 4: Supervised Classification (Naïve Bayes, LDA and KNN)* | 3 |
| Lesson 5: Supervised Classification (Support Vector Machine) | 3 |
| Lesson 6: Similarity Learning (Recommendation System) | 3 |
| Lesson 7: Unsupervised Learning (Cluster Analysis) | 3 |
| Lesson 8: Tree-Based Methods and Ensemble Learning (AdaBoost) | 3 |
| Lesson 9: Time Series Modelling or Text Mining | 3 |
| Lesson 10: Artificial Neural Network and Deep Neural Network | 3 |
* AI stands for artificial intelligence, ML stands for machine learning and DL stands for deep learning
** LDA stands for Linear Discriminant Analysis and KNN stands for K Nearest Neighbours
Instructors*
Ahmed Munieb Sheikh
Ahmed Munieb Sheikh is an enthusiastic educator with diverse experience in the fields of Computer Science, Machine learning, Computer Vision, Image Processing and Data Analytics. He received his Master of Science Degree in Computer Science with Distinction, from the University of Bedfordshire in the United Kingdom. He is also completing certification in Data Analytics, Big Data, and Predictive analytics at Ryerson University. Ahmed travelled to France and Spain to present his research work and projects. He has exposure and experience with the IT industries in UK, France, Spain, Pakistan and Canada. He has more than six years of teaching experience in computer science. Ahmed believes that we should never leave the learning path. He is passionate about making people’s lives better through working on machines and teaching. Whether it’s a small piece of functionality implemented in a way that is seamless to the user, or it’s a large scale effort to improve the performance and usability of software, he is there.
Chengliang Huang
Chengliang has a Ph.D. in Electrical and Computer Engineering and an MBA in Operation Management. He has instructed at various universities and colleges around the country for several years now. His areas of expertise include teaching statistical learning, regression analysis, business statistics, data analysis, data management systems, big data platforms, university mathematics and business management. Professionally, he was also a data scientist for a thriving data technology start-up.
*Subject to change without notice
Who Will Benefit From The Course
This course is beneficial for information architects who want to gain expertise in machine learning, recent graduates who are looking to build a career in machine learning, or anyone who wants to learn about machine learning to help make data-driven business decisions.
Dates
Live Online
Part Time:
- July 06, 2021 – Sept 07, 2021
- Students must devote at least 3 hours per week to attend live webinars
- Webinars will be held on Tuesdays from 4:00 to 7:00 pm PST
- Outside of live instructional periods, students will be expected to take part in various independent and/or group activities
Required Materials
None required. Optional resources may be selected by the instructor.
Fees
The registration fee for this course is $890.
Technical Requirements
Live Online Students
Ashton College uses web conferencing tools to help instructors and students connect and collaborate live online. For the online classes, students need to have a fully functional computer system with a webcam, speakers and microphone or headset and headphones, along with a reliable high-speed internet connection. Though the classes can be accessed using smartphones and tablets, we recommend using a laptop or desktop computer for a better learning experience.
This course does not require approval by the Private Training Institutions Branch of the Ministry of Advanced Education and Skills Training. As such, it was not reviewed.
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