Earn Your Masters Degree In Machine Learning and Data Science Online from Imperial College London
Introduction
Machine Learning is a data analysis method that automates analytical model building. It is a branch of artificial intelligence that works on the idea that computers can learn from data, identify patterns and execute actions with minimal human input. An online Master’s Degree in Machine Learning and Data Science from Imperial College London is designed to accelerate your software engineering or Data Science career, enabling you to choose a rewarding career path. This online course comes with hands on projects that will enable you to showcase your new skills with great portfolios.
About Imperial College London
Chartered in 1907, Imperial College is ranked #11th by World University Rankings 2021 and boasts of several world-renown researchers in artificial intelligence, Machine Learning, and Data Science, most of whom have contributed to the creation of this graduate degree in Machine Learning.
Machine Learning and Artificial Intelligence are increasingly becoming the most in-demand fields in the job market, and the goal of this online Master’s degree in Machine Learning is to meet this demand by providing practical training through guided experience to enable professionals to tackle real-world challenges as data scientists, Machine Learning Engineers, and computational statisticians.
About online Master’s Degree in Machine Learning and Data Science from Imperial College
This online Master’s degree in Machine Learning and Data Science is a rigorous degree with a deep focus on Machine Learning foundations. Before enrolling in this program, you are required to have an undergraduate degree in a relevant subject such as mathematics, computer science, statistics, physics, or economics. This program is extremely interactive, offering multiple opportunities to engage with the faculty at Imperial College London, a world-leading institution in Data Science, Machine Learning, and artificial intelligence.
The course offers hands-on projects that allow learners to build a portfolio showcasing their skills in deep learning, probabilistic modeling, unstructured data processing, and anomaly detection. This online graduate degree in Machine Learning and Data Science covers the foundations in statistics and mathematics as well as how to use industry-standard tools like PySpark to implement scalable Machine Learning solutions. You will also learn the ethics of Machine Learning applications.
Course prerequisites
As already mentioned, you need to have a quantitative bachelor’s degree in a subject like mathematics, computer science, statistics, physics, or economics. Alternatively, a professional or other qualification earned by written examinations may be accepted if approved by the faculty.
Expected Learning Outcomes
Graduates of this online Master’s degree in Machine Learning and Data Science from Imperial College will be equipped to take up roles as Machine Learning engineers, data scientists, natural language processing engineers, bioinformatics or health data scientists, data engineers, software engineers, or artificial intelligence engineers.
One of the best online Master’s degree in Machine Learning
Join an exciting, in-demand field with an online Master’s degree in Machine Learning and Data Science from one of the top-ranked universities in the world. In this flexible online program, you will develop an in-depth understanding of Machine Learning methods, alongside priceless practical skills as well as a guided experience to applying them to real-world challenges. The curriculum is designed to propel your Machine Learning and Data Science careers forward, allowing you to choose the right path for your career.
With multiple hands-on projects, you will graduate with the ability to go beyond the algorithms and turn data into useful insights, play a role in contributing to your organization’s decision-making process, and become a member of the lucrative and rapidly growing Machine Learning and artificial intelligence profession.
What makes this graduate degree in Machine Learning and Data Science from Imperial College unique
- Imperial College is home to world-renown mathematicians, including three winners of the Fields Medal
- With its famous mathematicians, Imperial College is home to deep thinkers capable of pioneering new research into today’s emerging scientific and technological challenges.
- Unlike other master’s degree programs that teach Machine Learning with a computer science focus, this graduate degree in ML equips learners with the mathematical and statistical theory needed to fully understand Machine Learning as well as the practical skills to handle real-world applications that they need to succeed in their careers.
- The degree trains learners in the computational, mathematical, and statistical foundations of Machine Learning, allowing them to critique data analysis while implementing scalable Machine Learning solutions.
- Learners have an opportunity to broaden their outlook by participating in a program-spanning module, the first of its kind, in ethics of Machine Learning and artificial intelligence transparency, covering aspects that offset potential limitations and biases introduced by Machine Learning.
- The coursework is designed to enable learners to develop an in-depth understanding of Machine Learning theories as well as invaluable practical skills in Python and R for solving real-world problems.
Learning method
This graduate degree in Machine Learning is offered online through the Coursera platform. Both the course and the online platform are designed to give you a flexible, seamless, and engaging learning experience. You will learn through a range of online methods such as:
- pre-recorded lectures and video capture
- asynchronous peer-to-peer and staff-moderated discussion forums
- discussion forums and office hours
- slide-decks with audio commentary
- practical exercises in coding and analysis
- synchronous scheduled live tutorials
- discussion prompts
You will also be required to participate in discussion boards as well as graded discussion prompts. For efficiency, you will be given core reading in order to develop your critical thinking and transferrable skills.
The assessment methods, also delivered through the Coursera platform, include:
- online quizzes
- video blogs
- case study review discussions
- coding exercises
- written reports
Course Duration
This online master’s degree in Machine Learning program is designed to take 2 years to complete.
Course Modules
Exploratory Data Analytics and Visualization – Core
This introductory module will provide you with the skills and knowledge required to produce convincing narrative summaries as well as informative visualizations for a range of complex datasets. You will learn how to assess and evaluate data structure and quality and master the techniques that uncover the underlying data structure, both for initial reporting to a range of intended audiences while providing guidance for potential formal analysis and model formulation. Both analysis and visualizations are implemented using R tidy verse packages.
Programming for Data Science – Core
This module equips learners with skills and knowledge required to support implementation, testing, and deployment of Machine Learning algorithms, and constructing data processing and analytic pipelines for Data Science. The module compares and contrasts the two most popular Data Science languages, R and Python, and learners will gain sound mastery of both languages for a range of challenges arising in data analysis tasks.
Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 1- Core
This module introduces learners to the ethical implications of the new capabilities offered by Artificial Intelligence and Data Science. It begins by discussing the ethical use of data itself – the building block of Data Science pipelines. It then discusses the principles that tech leaders as well as international bodies are adopting in an effort to promote ethical use of artificial intelligence and Data Science algorithms, including a discussion of real-world examples of failings and adverse outcomes. Finally, this module covers ways in which artificial intelligence and Data Science can usher in novel solutions moral predicaments of old, such as bias and prejudice, or even user in entirely novel moral dilemmas.
Applicable Maths – Core
This module introduces learners to the statistical and mathematical tools used in subsequent modules. It also reviews the basic probability and differentiation and integration. It also covers, in more detail, decomposition, eigenvalue, optimization techniques, and models of convergence.
Supervised Learning – Core
This module introduces learners to the framework of supervised learning. You will learn the framework of linear models and access examples of their extension to generalized linear models. You will also be introduced to the general principles of modeling as well as the models of failure you are likely to encounter when working with flexible, parametric models. Different non-parametric methods for regression and classification will looked into, and their performance evaluated on datasets from a range of scientific problem domains. Throughout the module, the emphasis will be on principled, uncertainty-aware modeling.
Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 2 – Core
This is the second part of this module
Big Data: Statistical Scalability with PyShark – Core
This module consists of three components – distributed programming, Spark, statistical analysis at scale. Big Data technology is considered the panacea of all data processing challenges. In this module, learners will explore and establish when it is appropriate to utilize Big Data technology for data analysis. Learners will also be introduced to statistical concepts like Bayesian parameter estimation with a large scale data and explore data sampling strategies in the world of Big Data.
Bayesian Methods – Core
This module introduces learners to subjective probabilities and the Bayesians paradigm for making coherent individual decisions during times of uncertainty. The module blends classical fundamental principles and mathematical rigor with a modern, high-level overview of a broad range of modern statistical techniques. Learners are introduced to computer software packages for implementing specific inferential procedures required for complex Bayesian analyses.
Unstructured Data Analysis – Core
This module provides learners with the knowledge and skills they need to handle “unstructured” data such as text, images, and network data. Data Science is full of problems that involve unstructured data and this module equips learners with methods for converting unstructured data to more familiar “structured” forms for use in Machine Learning methods as well as direct approaches with unstructured data. Examples include natural language processing and network analysis.
Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 3 – Core
This is the final part of this module
Unsupervised Learning – Core
This module provides learners with an in-depth introduction to the different challenges of unsupervised learning. Topics covered in this module include factor analysis, clustering, dimensionality reduction, outlier, anomaly, and change point detection.
Deep Learning – Core
This module introduces learners to the building blocks of deep learning models, and network architecture design for specific applications, in both supervised as well as unsupervised contexts. You will learn practical skills in implementing neural networks using PyTorch. You will learn how to use this framework to train and evaluate networks. The core focus of this module is on the mathematical and statistical foundations of some of the most complex deep learning models, such as generative adversarial networks (GANs), Bayesian methods for neural networks, and variational autoencoders (VAEs).
Learning Agents – Core
In an automated Machine Learning process, algorithms that make up both select and inference decisions can be referred to as learning agents. This module builds a learner’s expertise for taking Machine Learning beyond prediction process to formal decision making process.
Research Project – Core
This is a comprehensive research project that gives learners the opportunity to demonstrate what they have learned through this online Master’s degree in Machine Learning and Data Science.
Program Cost
£28,000
Master’s Degree in Machine Learning and Data Science from Imperial College: Final verdict
Online Master’s degree in Machine Learning and Data Science is a stackable and modular program that allows learners to start their degree learning with an open course. The flexible learning experience enables students from all over the world to access quality education that would otherwise be available to only those who are available for on-campus learning. Sign up for this Master’s degree in Machine Learning online today and set yourself up a rewarding and exciting career in Machine Learning and Data Science.