Machine Learning: Practical Applications
offered by LSE

Public University
1 Campus
Estd. 1900

Machine Learning: Practical Applications
at
LSE
Overview

Explore machine learning techniques to optimise your data analyses for informed business decision-making

Duration	8 weeks
Total fee	₹1.84 Lakh
Mode of learning	Online
Course Level	UG Certificate

Machine Learning: Practical Applications
at
LSE
Highlights

Earn a certificate of completion from The London School of Economics and Political Science

Machine Learning: Practical Applications
at
LSE
Course details

Skills you will learn

Business Analytics Regression Big Data

Who should do this course?

For Mid to senior managers, data specialists, consultants, analysts, and IT and business professionals
For Those who are interested in upskilling, transitioning into a data science role

What are the course deliverables?

Understanding of the core principles of machine learning

More about this course

This course focuses on the practical applications of machine learning in modern business analytics and equips you with the technical skills and knowledge to apply machine learning techniques to real-world business problems
First part of the course explores how to learn from data, introducing you to the core principles of machine learning
During the second part of the course, you?ll gain an in-depth understanding of a variety of machine learning techniques that you can apply when analysing big data including regression, variable selection and shrinkage methods, classification, tree-based methods, ensemble learning, unsupervised learning, and an introduction to neural networks
Understand how these methods can help data scientists, business leaders, analysts, and professionals problem-solve and innovate through informed, data-driven decision-making

Machine Learning: Practical Applications
at
LSE
Curriculum

Module 1

Learning from data

Module 2

Principles of machine learning

Module 3

Regression

Module 4

Variable selection and shrinkage methods

Module 5

Classification

Module 6

Tree-based methods and ensemble learning

Module 7

Introduction to neural networks

Module 8

Unsupervised learning

Machine Learning: Practical Applications
at
LSE
Faculty details

Dr Kostas Kalogeropoulos, Associate Professor of Statistics

Kostas’ research focuses on developing and applying advanced computational methods, such as Markov Chain and Sequential Monte Carlo, for Bayesian Inference. His methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion.

Dr Yining Chen, Assistant Professor of Statistics

Yining's current research focuses on developing new methods for statistical problems such as change-point detection and nonparametric estimation. He is also interested in understanding the computational aspects of statistical methods. He completed his PhD (2014) in Statistics at the University of Cambridge.

Dr Xinghao Qiao, Assistant Professor of Statistics

Xinghao’s research is focused on (i) functional and longitudinal data analysis, (ii) high dimensional statistical inference, e.g. covariance and precision matrix estimation, variable selection, (iii) time series analysis, e.g. functional time series, high dimensional time series, (iv) statistical machine learning with applications in Business, Neuroimaging Analysis and Environmental Sciences.