Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Machine learning, which combines the areas of statistics, optimization, computing, and data mining, is being embraced across sectors due to its capacity to solve issues in the availability of large data sets. The Machine Learning: Practical Applications online course covers a wide spectrum of machine learning methodologies, with a focus on artificial intelligence in current business analytics.
Modern machine learning achievements include its use in commercial activities like recommendation systems (like Amazon and Netflix), search engines, and promotion. In financial companies, machine learning technologies are rapidly being utilized for algorithm-based trading, forecasting consumer behaviour, risk, and compliance. The Machine Learning: Practical Applications Training from the London School of Economics provides a systematic approach to artificial intelligence in current business statistics.
The Machine Learning: Practical Applications syllabus engages with real-world challenges by implementing various machine learning models to large datasets in R, interpreting the predictions, and evaluating these predictions to guide the business.
The Highlights
- London School of Economics offering
- Split option of payment
- Downloadable resources
- Online learning
- 8-10 hours per week
- Course provider Getsmarter
- Projects and assessments
- 8 weeks duration
- Shareable certificate
- Self-paced learning
Programme Offerings
- Case Studies
- quizzes
- online learning
- Offline resources
- Self-paced learning
- Infographics
- video lectures
- Live polls.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | LSE |
Machine Learning: Practical Applications Fees Structure
Fee type | Fee amount in INR |
Machine Learning: Practical Applications fees | Rs. 107,131 |
Installment Plan
| 1st installment | Required before: 2025-02-25 | Amount Due: ₹53,566.00 INR |
| 2nd installment | Required before: 2025-03-27 | Amount Due: ₹53,565.00 INR |
Eligibility Criteria
Certification Qualifying Details
To qualify for the Machine Learning: Practical Applications certification, students will have to complete the course modules, present assignments, and projects, and engage in online school work including examinations, research reports, quizzes, live polls, and other instructional materials. Evaluation of students is based on a series of submitted projects and assignments. All of the guidelines listed in the course manual must be met in order to be certified. At the time of admission, the coursebook will be given to candidates.
What you will learn
After completing the Machine Learning: Practical Applications online training, learners will gain the strength to make better-informed decisions and solve problems by understanding the various machine learning techniques to apply to a variety of data sets. Students will learn to use various techniques of machine learning such as variable selection, regression, shrinkage approaches, dimension reduction, classification, and unsupervised learning. Candidates will also learn about the most recent advances in artificial intelligence, such as neural networks, and how they may be implemented in the business environment, as well as improved statistics and mathematics skills and the fundamentals of coding in R.
Who it is for
- Mid to top-level managers, data experts, data analysts, consultants, and business and IT professionals involved in integrating artificial intelligence into an organization for efficient data analytics.
- Candidates interested in upskilling, moving into a machine learning job, or gaining a deeper knowledge of data science's commercial implications across sectors and business domains.
Admission Details
To get admission to the Machine Learning: Practical Applications online course, follow the steps mentioned below:
- Open the machine learning course webpage.
- Click the ‘Register Now’ button to start the enrolment
- Read the terms and conditions carefully and agree with them to continue
- Create a profile on the course provider’s website
- Fill in the billing address If someone is paying your fee, provide sponsor details
- Pay the amount of fee and start the course
The Syllabus
- Identify applications of machine learning in different contexts
- Match data and problems with appropriate machine learning techniques to solve a given problem
- Recognise the different types of data
- Interpret given data through suitable visualisations
- Analyse data in R in preparation for machine learning applications
- Recognise machine learning models and the way they are fitted
- Demonstrate an understanding of the training error and test error functionality in machine learning performance
- Identify the role of the bias-variance trade-off and cross-validation as key parts of the machine learning pipeline
- Analyse the output of an appropriate machine learning model in R to inform decisions
- Identify the types of problems for which regression is a suitable machine learning approach
- Demonstrate an understanding of regression model mechanics and the problems they solve
- Apply the regression model to a suitable data set in R
- Analyse the prediction output of a regression model to inform a business decision
- Reflect on the assumptions and limitations of the regression model
- Demonstrate an understanding of the application of variable selection in relation to regression models
- Discuss the impact of variable selection on a regression model prediction
- Apply variable selection to a regression data set in R
- Describe the scope of application of shrinkage methods
- Implement shrinkage methods to a data set in R
- Investigate the potential of shrinkage methods in your context
- Identify the application of logistic regression in relation to classification models
- Apply logistic regression to a data set in R
- Analyse the prediction of logistic regression in R to solve specific problems
- Articulate an understanding of the practical application of generative models in relation to classification problems
- Evaluate the prediction of a generative model in R
- Recognise the important concepts of tree-based methods
- Demonstrate an understanding of the mechanics of tree-based methods
- Execute tree-based methods on a data set in R
- Identify the fundamentals of ensemble learning
- Apply ensemble learning to a data set in R
- Debate the improvement to a classification prediction following ensemble learning
- Discuss the use of neural networks to solve problems
- Recognise the model mechanics of neural networks
- Apply R code to train a neural network to make a prediction
- Interpret the predictions of a neural network model to inform business decisions
- Identify the specifics of dimension reduction and its application in analysing unlabelled data
- Use R to perform dimension reduction
- Explain the application of clustering models as a tool to find similarities in unlabelled data
- Distinguish between the different types of clustering models and their applications
- Debate the advantages and disadvantages of different types of clustering models
- Practise the fitting of a clustering model on a data set in R
- Recognise what you have achieved during this course
Instructors
LSE Frequently Asked Questions (FAQ's)
Speech recognition, image recognition, predictive analysis are the most used machine learning applications.
According to a report, almost every company is somehow using machine learning.
Machine learning is a field of computer science.
Machine Learning: Practical Applications course offered by LSE is one of the best courses for learning applications of machine learning.
The Machine Learning: Practical Applications course is 8 weeks long.