Learning about the construction of machine learning and artificial intelligence algorithms is not a simple process. These are some of the most elaborate and sophisticated concepts you’ll see in the computer science field. They’re based on complex mathematical and statistical modeling, as well as logical and technical processes.
Algorithm work is part of the vanguard progress of a world in which data scientists are in high demand. Mastering this field requires a lot of learning and training, because of the technical complexity that it involves. Neural networks and other AI/ML models are built on some advanced ideas of how computer science works and what it has to offer.
Here are four excellent resources for students who want to advance their knowledge of algorithms and related data structures.
- Data Structures and Algorithm Specialization — University of California San Diego
- Algorithm Specialization — Stanford
- Algorithms: Part One — Princeton University
- Introduction to Discrete Mathematics for Computer Science Specialization — University of California San Diego
This course involves hands-on work with algorithm development in order to help the student to get acquainted with how to evaluate and explore machine learning algorithms. It provides that framework for moving further into ML/AI and algorithm engineering.
In this course, students will directly implement algorithms in coding scenarios, initiating dozens of relevant tasks, to get an in-depth idea of the algorithm as code. Planners have invested thousands of hours in this challenging course in which students will learn to debug programs and evaluate a codebase according to its algorithmic capabilities. (Want to learn about the life of a data scientist? Check out Job Role: Data Scientist.)
In terms of topical coverage, this course covers both big networks and genome assembly, with interactive formatting that gets students close to the heart of what professionals do in a production environment. With this type of practical learning, students build a base of working knowledge involving how to set up and refine algorithms for ML/AI.
Students should have basic knowledge of one or more programming languages including Java, Python and C++.
Here’s another course that adequately prepares students for a greater role in exploring algorithms’ development and use. This course will also show major aspects of the context of machine learning development with in-depth implementation work on algorithms.
Part of the approach here is to enable graduates to “speak the language” of algorithm development. From security protocols to logical regression and classification techniques, professionals who can hold their own in these sorts of conversations will learn further on the job and advance their reputation as a thought leader in machine learning processes.
This course looks at the big picture and iterative implementation in order to help the student to gear up for this kind of technical expertise.
This is an intermediate level course with a flexible schedule.
This course, which comes from a top Ivy League source, covers many of the fundamental aspects of algorithm development that center on data structure work.
The philosophy here is that the fundamental understanding of algorithms relies on knowing more about the building blocks of which they are made. From random forests and decision trees to elaborate black box systems like echo state machines and Boltzmann machines, algorithm development works on the process of manipulating data in iterative and sometimes recursive ways.
Part one of this course, therefore, will go over elementary data structures and sorting, whereas part two will focus on graph and stream processing algorithms. Students will become comfortable with assessing data structures, how they are set up, and how they are used by machine learning programs. (Do you have an interest in creating software? Then check out 6 Software Development Concepts You Can Learn Through Online Courses.)
It’s not hard to see how this type of survey course prepares students for a working career in data science. Starting with data structures and in-depth analysis, students work further into the nuts and bolts of how to use the conceptual means to build the practical result.
Introduction to Discrete Mathematics for Computer Science Specialization — University of California San Diego
Under many of the techniques that facilitate algorithm development lies mathematical modeling. This specialized course will focus on discrete math as a component of an engineer’s toolset. Understanding the mathematical properties of data structures is a key skill for data scientists and others involved in algorithm work.
Starting with basic probability and number theory, this course will move students along the path to further understanding discrete mathematics and its application to algorithm production. Students will learn about basic algorithm techniques and sorting, and get hands-on experience trying to solve problems.
They will look at graph and string algorithms and their application, for example, in human genome work. Students will also look at the use of tools like binary search trees, hash tables, queues and stacking and work toward advanced problem-solving with linear programming and approximate algorithms.
All four of these courses provide their own key approaches to a rapidly emerging professional field that’s inaccessible to many people because of its difficulty. Not everyone can be a data scientist, but those who feel they are qualified and ready to learn can utilize these course offerings to build up their technical knowledge to fit their logical and deductive ambitions.