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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
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