Technology and modernization have undoubtedly brought about remarkable developments in various global industries such as science and technology, education, our health care system, and even our overall perspective on what it means to be an intelligent entity---to be human.
Artificial Intelligence creates a vast and extensive field of study. Its ever increasing grounded and real-life applications continue to modify our perspective of the world and of course, provide improvements to our lives.
The history of AI stretches way back in decades when Alan Turing asked the question “can machines think?” This question forever changed history and science. Since then, AI has became a groundbreaking field under computer science. Throughout the years, the field has categorized areas of specializations to intensively cater to specific subject matters under AI. One of the focus area of study is Machine Learning.
Machine learning plays a significant role in AI. It offers an initial and basic way for machines to become intelligent. That is to teach a computer program or system to make accurate predictions from massive data and ultimately perform its assigned task.
Studying the fundamentals of machine learning can give you foundational knowledge and skills that you can use to pursue other subject matters under AI. If you want to build your career in the AI industry, you’ll definitely find the following online courses on machine learning helpful.
So let’s take a look at the top 6 machine learning courses offered by edX that will spark your interest further and assist you in gaining a competitive edge in your AI career.
Learning the fundamentals of AI should be your initial step as it will give you a comprehensive and overview of everything you will be studying on machine learning. This 10-week course from University of California San Diego edX will do just that.
As part of the Data Science MicroMasters program, this course will teach you all about the theories of algorithms and predictive models. These theories play a huge role in programming smart machines. The program also includes real-world case studies to give you hands-on training on the various applications of algorithms in creating effective and smart AI.
The course offers five specific topics:
(1) Classification, regression, and conditional probability estimation;
(2) Generative and discriminative models;
(3) Linear models and extensions to nonlinearity using kernel methods;
(4) Ensemble methods: boosting, bagging, random forests; and
(5) Representation learning: clustering, dimensionality reduction, auto-encoders, and deep nets.
The program’s schedule takes around 8-10 hours a week.
This program from IBM is another introductory course on machine learning but unlike the previous course, it exclusively uses Python in the course. Python is an approachable and well-known programming language structure. Similar to the previous course, it offers a comprehensive syllabus structure that puts together all the fundamental topics under machine learning, along with hands-on training and real-life applications as you move forward through the course.
The course is a self-paced 5-week program with 5 modules, with each new module introduced and discussed in a weekly basis. This is an ideal schedule for working professionals who want to learn about machine learning and AI without compromising much of their present career or advanced studies.
To give you an idea of the specific topics that will be discussed under the course, here is the program’s syllabus:
Module 1: Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning
Module 2: Regression
Model evaluation methods
Module 3: Classification
Support Vector Machines
Module 4: Unsupervised Learning
Module 5: Recommender Systems
Content-based recommender systems
(tabulated from https://www.edx.org/course/machine-learning-with-python-a-practical-introduct)
This is a 6-week complementary course from IBM, ideally taken after gaining enough knowledge on other machine learning courses. This is because capstone programs require intensive and extensive real-world applications. As you gather more knowledge and skills from other programs, you will find it easier to work on capstone projects.
A total of 5 specific topics will be studied under the course:
(1) applying your knowledge of data science and machine learning to a real life scenario;
(2) analyzing and visualizing data using Python;
(3) performing a feature engineering exercise using Python;
(4) building and validating a predictive machine learning model using Python; and
(5) creating and sharing actionable insights to real life data problems.
The program is self-paced with online tutorials of 3 to 4 hours per week.
Quantum machine learning is a growing interdisciplinary subfield under AI. It incorporates the concepts, theories, and principles behind quantum physics research and technologies to machine learning. Its overall goal is to create quantum-enhanced machine learning mechanisms for more effective and smarter AI.
This program created by University of Toronto edX discusses 4 specific lessons to give students a better understanding of the subfield:
(1) distinguishing between quantum computing paradigms relevant for machine learning;
(2) assessing expectations for quantum devices on various time scales;
(3) identifying opportunities in machine learning for using quantum resources; and
(4) implementing learning algorithms on quantum computers in Python.
The course is a 9-week program that can be completed with a schedule of 6-8 hours a week at your own pace.
Since machine learning is a popular and widely used methodology in data science, it is important to study their fundamental correlations. In this 8-week and self-paced course made by Harvard University edX, you will be studying the basics of machine learning, performing cross-validation, learning about several popular machine learning algorithms, building recommendation systems, and learning all about regularization and its uses.
It is also a Professional Certificate Program in Data Science that can give you a competitive edge in your career.
Machine learning is also widely applied in the market industry. Amazon Web Services offers a program that can train students with the necessarily machine learning knowledge and AI skillset to help them grow this industry.
The course is a 4-week self-paced program consisting of various lessons and activities. Overall, the course has 4 specific topics:
(1) key problems that Machine Learning can address and ultimately help solve;
(2) how to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance;
(3) how to publish a model using Amazon SageMaker; and
(4) how to integrate the published SageMaker endpoint with an application.