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Quick Facts

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

Course Overview

Throughout this programme, the candidates will enhance their skills in machine learning and will learn to apply these skills to problems of the real world, related to the field of business. This online programme of Imperial Machine Learning for Decision Making is provided to the candidates through the platform of Emeritus and is brought to them by the Imperial College Business School.

The participating candidates will gain exposure to analytical techniques, for example, regression, classification and clustering and learn how to apply these techniques to real-life issues arising in the field of managing a business. The students will be provided with opportunities to have conversations with experts who have deep technical expertise about the subject, which will ultimately help them in having a greater impact on the company they work in.

This course is for a duration of 10 weeks wherein the students will not only be learning the theoretical know-how but also the practical applications of the concepts learnt.

The Highlights

Support team deployed 24x7
Expert interviews
14-day money-back guarantee
Duration of 10 weeks
Study of 4 to 6 hours/week
Certification offered by Imperial College Business School
Course offered by Emeritus

Programme Offerings

Goal setting
Expert support
Orientation
LIVE Webinars
Follow ups
video lectures
Interactive activities
Online Classrooms
continuous programme access

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Imperial College Business School, London

Eligibility Criteria

Work Experience

It is mandatory to mention the work experience one possesses at the time of registering. After that, no information is given about mandatory work experience.

Education

Candidates are required to have prior knowledge about linear algebra, statistics, and probability.

Certification Qualifying Details

A digital certificate that is verified will be received by eligible candidates (those who have completed the module) in their emails that will be awarded to them by the Imperial College Business School.

What you will learn

Machine learning

The students who apply for this programme will graduate with the following learning outcomes:

Apply the concepts of machine learning to practical use
Explain the unavailability of meaningful conclusions from experiences
Calculate what probability a function has to provide an accurate outcome
Predict a model’s performance by selecting the best fit on the basis of the training set and the validation set
Find the difference between ranking and production problems
Evaluate the problems of regression with the help of performance measures
Improve the rate of miscalculation on interesting cases by using oversampling
Calculate conditional probabilities and find their applications in real life by using Naïve Bayes Theorem
Solve real-life problems by utilising classification and regression trees
Define proximity for clustering methods

Who it is for

This course is highly recommended to:

Those managers that want to inculcate better strategies into their company’s existing digital media efforts
Managers that are involved in discussions related to the operations of marketing and technology.
Those managers and consultants that want to view digital media marketing from the perspective of a manager
Those professionals that have expertise in areas related to banking, advertising, consulting, FMCG, financial services, healthcare, retail, and IT

Admission Details

Those who want to pursue the course of Imperial Machine Learning for Decision Making should follow the given procedure.

Step 1: The following link has to be followed to reach the course overview page: https://execed-online.imperial.ac.uk/machine-learning

Step 2: At the end of the page, you can find the option of ‘Apply Now.

Step 3: After clicking on the Apply Now button, you will be taken to a page where your login details will be asked. Provide your login information. In case you are a new user, you have to register on the site.

Step 4: The next page will ask for your names, contact information, work experience, etc. Fill in as accurately as possible and proceed further.

Step 5: The payments page will consist of three payment methods. Choose the method that is best suited to you and complete the process of admission by paying with a wired account or credit or debit card.

The Syllabus

What is machine learning?
The machine learning process
The machine learning landscape
Machine learning in the real world

Is learning feasible at all?
Interpreting the bound
A probabilistic setting
When is machine learning feasible?

Which fit is "right"?
Test set
Validation set
The "Training set - Validation set - Test set" approach

Performance measures for regression
Lift charts for classification problems
Problems (use confusion matrix)
Lift charts for regression problems

Oversampling
K-fold cross-validation

K-nearest neighbours for classification
Binary and categorical predictors
K-nearest neighbours for regression
Distance functions
How should we choose k?

Motivation
Exact Bayes classifiers
The Laplace Estimator
Bayes' Theorem
Naïve Bayes classifiers

Classification Trees
Choosing the best split: Part 2
Regression trees
Random forests and boosting algorithms
Choosing the best split: Part 1
Pruning a classification tree
Bagging

Motivation
Hierarchical clustering is myopic
Practical concerns of a cluster analysis
Hierarchical clustering
K-means clustering
Analysis

Imperial College Business School, London Frequently Asked Questions (FAQ's)

1: How can I avail a discount on this course?

Candidates can share their referral codes with their colleagues and get them to join the programme. By doing this, they will be able to gain a discount of 160 pounds for every person that joins using their referral code.

2: Who is assigned as an instructor for this course?

PROFESSOR WOLFRAM WIESEMANN, who is the Professor of Analytics and Operations at Imperial College Business School is assigned as the instructor who will be helping the students with this course.

3: Will I be given any discount if I am joining with a group?

Candidates can write to group-enrollments@emeritus.org for more details about group enrolment and special pricing. A discount of 20% can be availed if the candidates join with a group of candidates.

4: Where can I get my queries solved?

Candidates can reach us regarding their queries at +442033899722 or email at info@emeritus.org. The queries will be answered within 24 hours on weekdays and within 72 hours in case of weekends.

5: What are the challenges of implementation of this programme?

There are some challenges that may arise when the concepts of this programme are implemented. These challenges include lack of skilled employees, concerns with data quality and scope, and difficulty understanding use cases.

6: How many credits is this programme worth?

Since this course of Imperial Machine Learning for Decision Making is a certification programme, it is not accredited. However, one can apply for the academic credit from this programme to the Imperial MSc in Business Analytics, if they wish to pursue higher education in business analytics.

7: What do you mean by ‘Follow Up’?

A follow up is deployed by the Emeritus team that checks whether the participant is able to keep up with his/her assignments and submit them or time. This is known as follow up.

8: What are the pre-requirements of this course?

This course requires prior knowledge of linear algebra, statistics, and probability. No knowledge about programming is required. However, please note that all the course material will be in English and hence the submissions have to be in the English language only (except for non-English courses, such as Spanish).

9: How many classmates will I be studying with?

Although the total number of candidates is about 600, a typical classroom will consist of about 150 students belonging to diverse groups from over 70 countries.

10: Can I complete this course if I am already a working professional?

The course requires only about 4 to 6 hours of study per week; hence time has to be adjusted such that your work does not clash with your studies. The follow-up team will be there to keep track of your progress and help you keep up with the programme.