Data Structure & Algorithm
Showing 73–84 of 121 results
NYUx: Network Security – Protocols
Learn more fundamentals of network security, including cryptographic algorithms used in networking protocols, TLS/SSL, IPSec Layer 2 Security and Wireless Security.
PennX: Robotics: Vision Intelligence and Machine Learning
Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment.
RaspberryPiFoundation: Teach teens computing: Functions and algorithms, searching and sorting in Python
Take your Python skills further in this online course, guided by the Raspberry Pi Foundation.
SDA_Bocconi: Fundamentals of Python
We will equip you with everything you need to properly start using Python in your daily work activities. You will learn how to install Python and work with it through different graphical front-ends. You will then learn how to define objects and how to recognize different characteristics and functionalities. Finally, you will learn how to make Python execute a series of instructions in a sequential order through loops, as well as how to write your own functions.
SDA_Bocconi: Fundamentals of Python
We will equip you with everything you need to properly start using Python in your daily work activities. You will learn how to install Python and work with it through different graphical front-ends. You will then learn how to define objects and how to recognize different characteristics and functionalities. Finally, you will learn how to make Python execute a series of instructions in a sequential order through loops, as well as how to write your own functions.
SNUx: Introduction to Optimization
A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.
StanfordOnline: Algorithms: Design and Analysis, Part 1
Welcome to the self paced course, Algorithms: Design and Analysis! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience.
StanfordOnline: Algorithms: Design and Analysis, Part 1
Welcome to the self paced course, Algorithms: Design and Analysis! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience.
StanfordOnline: Algorithms: Design and Analysis, Part 2
Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience.
StanfordOnline: Automata Theory
This course covers the theory of automata and languages. We begin with a study of finite automata and the languages they can define (the so-called "regular languages." Topics include deterministic and nondeterministic automata, regular expressions, and the equivalence of these language-defining mechanisms.
StanfordOnline: Mining Massive Datasets
The course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course.
Statistics.comX: Applied Data Science Ethics
AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.