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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

Artificial intelligence and machine learning advances run hand in hand with the degree to which quantum computing footprint improves. Thus, it is natural to leverage machine learning capabilities with the advent of quantum computing. This Quantum Machine Learning certification course will explore the underlying principles associated with futuristic technologies.

The program on Quantum Machine Learning encompasses the advantages of machine learning once it is coupled with futuristic quantum technologies. These have the potential to provide the much-awaited advancements of this industry. Some examples include machine learning and select frameworks in Python.

This Quantum Machine Learning training program is intended to be delivered across 9 weeks along with an input of 6-8 hours per week by the participants on their own time for optimum results. The University of Toronto is the partner institution for this self-paced course. The video transcripts are in English and the students have an option to get a paid plus official certification.

The candidates who enroll in any track of the course will be able to progress at their speed. Both tracks - audit and verified may be opted for by the candidates for finishing the course. If the candidates are interested in learning for free without a certificate they may choose the audit track which is open for a limited period of access. For unlimited access, the students should switch over to the verified track by paying some fees.

The Highlights

Machine learning focus
Free course for a limited period
Employable learning outcomes
Run time of 9 weeks
Corporate packages available on request
6 to 8 hours of weekly effort needed
Recently updated curriculum
Self-paced programme pedagogy

Programme Offerings

Premium machine learning outcomes
9-week completion
Self-paced layout
Mobile courseware
Official certification

Courses and Certificate Fees

Fees Informations	Certificate Availability	Certificate Providing Authority
INR 4082	yes	University of Toronto, Toronto

This stage applies only to the students who choose to take on the certified track, and then will have to pay the Quantum Machine Learning certification fee mentioned below:

Type of Fee	Amount in INR
Certification Fee	Rs. 4,052

Eligibility Criteria

Work experience

Exposure to statistical mechanics, machine learning, or quantum physics is applicable for the Quantum Machine Learning certification by edX.

What you will learn

Machine learningKnowledge of Python

The Quantum Machine Learning certification course has the following elements at its core and is taught effectively throughout the program delivery:

Basic conceptual knowledge associated with understanding the founding principles associated with quantum states in the context of generalization of probability distributions
Get equipped with foundational aspects pertaining to closed and open-loop systems and measurements as a form of sampling tailor-made for specific situations to achieve optimal performance
Application of Quantum computation protocols and contrast them with paradigms in the computing world and their corresponding implementations
Working fundamentals associated with variable circuits and encrypting classical information onto quantum-based systems with an ability to incorporate hybrid learning algorithms
Incorporate the creation of computer paradigms with discrete optimizations as necessary for supervised and unsupervised learning on common kernel functions
Deep dive into coherent learning protocols involving an accurate measure of their resource requirements and a summary of the quantum phase estimation
Develop a robust foundation that enables the implementation of the aforementioned algorithms for various Gaussian processes using Python.

Who it is for

Python Programmer

The program on Quantum Machine Learning will benefit the following sets of people the most:

Those who are comfortable with at least one of the following subjects: Complex numbers, Linear algebra, Calculus, or Python programming
Professionals Python Programmers with mastery in one of the following segments: Quantum physics, Machine learning, or Statistical mechanics

Admission Details

Please refer to the section below to get a sense of the enrolment process for the Quantum Machine Learning classes

Account login/creation phase

To begin with the enrolment phase, please use the “Enroll” red button at the top of the program webpage. A new tab should open up in your browser. You are required to either login into your existing edX account or create a new one. At this point, existing customers will find themselves logged in. For the rest, the on-screen pathway will guide you towards creating an account. During this process, the following information will be solicited from your side:

First Name and Last Name
Public username
Strong password
Functional email address
Current country of residence

Track selection phase

The Quantum Machine Learning program has two distinct tracks. This second step is related to that. Both are explained in the following text:

Official Certification path: The first option is for those professionals who desire to utilize this program in their career growth. Official certification from the education partner will go a long way in establishing your credential. Should you choose this path, there will be a fee that will apply
Audit pathway: On the other hand, for casual learners, the audit pathway works just fine as it gives them 6-week access to the program content and their progress. This route is free and needs to be planned by the participants in accordance with the expiry date

Payment phase

The last stage is where the incumbents pay for their choice of program. Needless to say, this will apply to only those candidates who choose to run with the certification route. The following content sheds some light:

To begin with, the audit route does not need payment. However, once enrolled, should the student want to upgrade to unlimited access, a fee is payable. In the general setting, the course validity is clearly mentioned on the dashboard
Conversely, for the participants choosing the certified path. The preferred payment mode is Credit Card. American Express cards are also compatible. Of late, the functionality of using PayPal is also offered

Our help database is exhaustive and does answer most of the questions. However, should you need more support, please reach out to us and we will endeavour to resolve it at the earliest

The Syllabus

Evaluation process

In order to gain the official certification, the participant must achieve 60% or higher in the course.

Your coding problems (12 in total) will account for 70% of your total grade.

The end-of-unit exams (4 in total) will account for 20% of your total grade.

Check-in quizzes found with videos (approximately 24 in total) will account for 10% of your total grade (you will be allowed to drop 4 of these).

Instructors

University of Toronto, Toronto Frequently Asked Questions (FAQ's)

1: I have never programmed before or am not familiar with Python. Can I learn Python programming during this courseware?

In our opinion, that will be difficult. Over 60% of the grades are earned from programming assignments for which an intermediate level of Python is required

2: Is this program going to teach machine learning?

No. Many high-quality resources are available to teach machine learning. This course only focuses on quantum computing and how it can be used in certain machine learning algorithms.

3: Can I complete this Quantum Machine Learning online course without a physics background?

The course was designed without assuming a physics background. The vocabulary can be intimidating, especially in the beginning: it is not an easy course, but patience will go a long way

4: Is this course self-paced?

edX requires a fair bit of independence on behalf of the students. The lectures are short and high-level, whereas the lecture notebooks are detailed and lengthy, explaining all concepts in formulas and code. Connecting the two might require you to read up on a specific part that you do not understand well

5: Vocareum does not grade/does not work/times out. What should I do?

Please make sure that you have read the instructions on using Vocareum, especially the troubleshooting part. If all else fails, contact them directly at support@vocareum.com, giving them the details of what happened, which browser you tried, and the course code

6: Is there a credit option available for this program?

At edX, we don’t have a credit scheme. You are requested to make your own arrangements for the same

7: Which payment modes are acceptable?

Credit cards from all major banks, including American Express. This program also accepts payments from PayPal

8: Is it recommended to undertake this program while keeping a job?

Yes, this program is a self-paced one. Be aware of the time requirements to make the most out of the learning experience. As long as you can make that time, you will be fine

9: What happens in the discussion portals?

They are a form of soliciting student feedback and also for asking questions regarding the courseware. It is monitored by the faculty

10: Is there a demo class available?

No, a demo isn’t available. You are free to audit the program at no cost