Online Courses and Tutorials

Onlinecourses.tech provides you with the latest online courses information by assisting over 45,000 courses and 1 million students.

Learn programming, marketing, data science and more.

Get started today

Skip to main content

Featured Post

Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

About this course: The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Who is this class for: Learners with at least a little bit of programming experience who want to learn the essentials of algorithms. In a University computer science curriculum, this course is typically taken in the third year.

Created by:   Stanford University

Basic Info
Course 3 of 4 in the Algorithms Specialization.
LevelIntermediate
Commitment4 weeks of study, 4-8 hours/week
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.9See what learners said
Syllabus
WEEK 1
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm. 

16 videos4 readings
  1. Reading: Week 1 Overview
  2. Reading: Overview, Resources, and Policies
  3. Reading: Lecture slides
  4. Video: Application: Internet Routing
  5. Video: Application: Sequence Alignment
  6. Video: Introduction to Greedy Algorithms
  7. Video: Application: Optimal Caching
  8. Video: Problem Definition
  9. Video: A Greedy Algorithm
  10. Video: Correctness Proof - Part I
  11. Video: Correctness Proof - Part II
  12. Video: Handling Ties [Advanced - Optional]
  13. Video: MST Problem Definition
  14. Video: Prim's MST Algorithm
  15. Video: Correctness Proof I
  16. Video: Correctness Proof II
  17. Video: Proof of Cut Property [Advanced - Optional]
  18. Video: Fast Implementation I
  19. Video: Fast Implementation II
  20. Reading: Optional Theory Problems (Week 1)
Graded: Problem Set #1
Graded: Programming Assignment #1
WEEK 2
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).  

16 videos2 readings
  1. Reading: Week 2 Overview
  2. Video: Kruskal's MST Algorithm
  3. Video: Correctness of Kruskal's Algorithm
  4. Video: Implementing Kruskal's Algorithm via Union-Find I
  5. Video: Implementing Kruskal's Algorithm via Union-Find II
  6. Video: MSTs: State-of-the-Art and Open Questions [Advanced - Optional]
  7. Video: Application to Clustering
  8. Video: Correctness of Clustering Algorithm
  9. Video: Lazy Unions [Advanced - Optional]
  10. Video: Union-by-Rank [Advanced - Optional]
  11. Video: Analysis of Union-by-Rank [Advanced - Optional]
  12. Video: Path Compression [Advanced - Optional]
  13. Video: Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]
  14. Video: Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]
  15. Video: The Ackermann Function [Advanced - Optional]
  16. Video: Path Compression: Tarjan's Analysis I [Advanced - Optional]
  17. Video: Path Compression: Tarjan's Analysis II [Advanced - Optional]
  18. Reading: Optional Theory Problems (Week 2)
Graded: Problem Set #2
Graded: Programming Assignment #2
WEEK 3
Week 3
Huffman codes; introduction to dynamic programming. 

11 videos1 reading
  1. Reading: Week 3 Overview
  2. Video: Introduction and Motivation
  3. Video: Problem Definition
  4. Video: A Greedy Algorithm
  5. Video: A More Complex Example
  6. Video: Correctness Proof I
  7. Video: Correctness Proof II
  8. Video: Introduction: Weighted Independent Sets in Path Graphs
  9. Video: WIS in Path Graphs: Optimal Substructure
  10. Video: WIS in Path Graphs: A Linear-Time Algorithm
  11. Video: WIS in Path Graphs: A Reconstruction Algorithm
  12. Video: Principles of Dynamic Programming
Graded: Problem Set #3
Graded: Programming Assignment #3
WEEK 4
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees. 

10 videos3 readings
  1. Reading: Week 4 Overview
  2. Video: The Knapsack Problem
  3. Video: A Dynamic Programming Algorithm
  4. Video: Example [Review - Optional]
  5. Video: Optimal Substructure
  6. Video: A Dynamic Programming Algorithm
  7. Video: Problem Definition
  8. Video: Optimal Substructure
  9. Video: Proof of Optimal Substructure
  10. Video: A Dynamic Programming Algorithm I
  11. Video: A Dynamic Programming Algorithm II
  12. Reading: Optional Theory Problems (Week 4)
  13. Reading: Info and FAQ for final exam
Graded: Problem Set #4
Graded: Programming Assignment #4
Graded: Final Exam
How It Works
Coursework
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.


Comments

Popular posts from this blog

Hands-on Text Mining and Analytics by Yonsei University

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.



LevelIntermediateLanguage English, Subtitles: Chinese (Simplified) How To PassPass all graded assignments to complete the course.
Syllabus

Big Data Hadoop Certification Training

Big Data Hadoop training will make you an expert in HDFS, MapReduce, Hbase, Hive, Pig, Yarn, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain. You will get Hadoop certification at the end of the course

About the Training
This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get Hadoop certification after the project completion.

Training Objectives
The hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to: Master the concepts of HDFS and MapReduce frameworkUnderstand Hadoop 2.x ArchitectureSetup Hadoop Cluster and write Co…

Learn to Program and Analyze Data with Python

About This Specialization This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization. Created by: 5 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or LinkedIn. Courses

An Introduction to Interactive Programming in Python (Part 1)

About this course: This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game "Pong".
Who is this class for: Recommended Background - A knowledge o…

Front-End JavaScript Frameworks: Angular

About this course: This course concentrates mainly on Javascript based front-end frameworks, and in particular the Angular framework (Currently Ver. 4.x). This course will use Typescript for developing Angular application. Typescript features will be introduced in the context of Angular as part of the exercises. You will also get an introduction to the use of Angular Material and Angular Flex-Layout for responsive UI design. You will be introduced to various aspects of Angular including components, directives and services. You will learn about data binding, Angular router and its use for developing single-page applications. You will also learn about designing both template-driven forms and reactive forms. A quick introduction to Observables, reactive programming and RxJS in the context of Angular is included. You will then learn about Angular support for client-server communication and the use of REST API on the server side. You will use Restangular for communicating with a server sup…

Программирование на Python

About this course: Python – простой, гибкий и невероятно популярный язык, который используется практически во всех областях современной разработки. С его помощью можно создавать веб-приложения, писать игры, заниматься анализом данных, автоматизировать задачи системного администрирования и многое другое. “Программирование на Python” читают разработчики, применяющие Python в проектах, которыми ежедневно пользуются миллионы людей. Курс покрывает все необходимые для ежедневной работы программиста темы, а также рассказывает про многие особенности языка, которые часто опускают при его изучении. В ходе курса вы изучите конструкции языка, типы и структуры данных, функции, научитесь применять объектно-ориентированное и функциональное программирование, узнаете про особенности реализации Python, научитесь писать асинхронный и многопоточный код. Помимо теории вас ждут практические задания, которые помогут проверить полученные знания и отточить навыки программирования на Python. После успешного о…

Launch Your Career in Data Science

A nine-course introduction to data science, developed and taught by leading professors.
About This Specialization Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Created by: Industry Partners: 10 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn.

Master of Computer Science in Data Science

A flexible and affordable degree from one of the top Computer Science programs in the world, focused on one of the hottest fields of the new millennium

Enroll in the Master of Computer Science in Data Science (MCS-DS) and gain access to the computational and statistical knowledge needed to turn big data into meaningful insights. Build expertise in four core areas of computer science—data visualization, machine learning, data mining, and cloud computing—while learning key skills in statistics and information science. This completely online degree is an affordable gateway to one of the most lucrative and fastest growing careers of the new millennium. The MCS-DS is offered by CS @ ILLINOIS, a U.S. News & World Report top five CS graduate program, in collaboration with the University’s Statistics Department and top-ranked iSchool. Join our alumni network of entrepreneurs, educators, and technical visionaries, who have revolutionized the way people communicate, shop, conduct business,…

Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Introduction to Data Science in Python

About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Ne…

Archive