The 10 best Data Structures & Algorithms courses in 2026

The second course introduces advanced evolutionary computation methods for intelligent optimization and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of genetic algorithms and particle swarm optimization. In this course, the configuration of intelligent optimization algorithms, handling constraints and multiple objectives, as well as combinatorial optimization, is introduced. Since almost all design problems in science and engineering can be formulated into optimization problems, after this course, you will become a pro

In this course, you will learn new data structures for efficiently storing and retrieving data that is structured in an ordered sequence. Such data includes an alphabetical list of names, a family tree, a calendar of events or an inventory organized by part numbers. The specific data structures covered by this course include arrays, linked lists, queues, stacks, trees, binary trees, AVL trees, B-trees and heaps. This course also shows, through algorithm complexity analysis, how these structures enable the fastest algorithms to search and sort data.

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive deep into the foundational concepts of Artificial Intelligence (AI) and its application in game development. By exploring AI techniques in Unity, you’ll learn how to implement AI-driven characters, create realistic movement, and develop advanced pathfinding systems. As you progress, you will discover how mathematics, physics, and algorithms

This course is your gateway to mastering Android game development with Unity 2022. You'll start by learning how to set up Unity and develop a basic understanding of the platform. From there, you will dive into building a simple yet engaging 3D endless runner game, learning how to create game levels, control player movement, and handle game mechanics such as platform spawning and object destruction. Next, the course takes you on an exciting journey to develop a 3D zigzag game, guiding you through advanced mechanics like player control via touch input, spawning platforms with random algorithms,

This course introduces learners to the core principles of artificial intelligence, including its history, definitions, and the role of data in AI. You’ll explore algorithms, specialized hardware, and delve into machine learning and deep learning fundamentals. Ethical considerations around AI, especially generative AI (GenAI), will also be discussed, ensuring you understand the broader impact AI has on society and the workforce. With clear explanations and real-world examples, the course ensures you not only learn theoretical concepts but also understand their practical implications. You’

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine

Build your subject-matter expertise This course is part of the AI in Media specialisation. When you enrol in this course; you'll also be enrolled in this Specialisation. ● Learn new concepts from industry experts ● Gain a foundational understanding of modern strategic tools ● Develop job-relevant skills with hands-on scenario-based projects ● Earn a shareable career certificate About this Course In a world where content is tailored to users, understanding recommendation algorithms is vital for any media professional. This course explores how organisations use machine learning to build 'mode

How can reinforcement learning scale beyond small tabular problems to high-dimensional environments such as games, robotics, and autonomous decision-making? This course introduces deep reinforcement learning, where reinforcement-learning algorithms are combined with neural-network-based function approximation. Learners begin by studying why tabular methods break down in large or continuous state spaces and how value functions, action-value functions, and policies can be represented by parameterized models. The course then develops value-based deep reinforcement learning methods, including fit

In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same s

This Teach Out does not issue certificates of completion. Algorithms – and algorithmic bias – are making regular appearances in the news, and increasingly, are being recognized as a policy issue. But what is an algorithm, exactly? And what does it mean when someone describes an algorithm as biased? This Teach-Out will encourage policy makers, agency leaders, and others in similar positions to identify algorithms that are already in use and make connections to broader ideas about fairness, justice, and equity. After completing the Teach-Out, learners will be able to participate in discussions