Update README.md

Author: jpa99

README.md

@@ -147,7 +147,7 @@ If you're interested in expanding your knowledge of algorithms, I've included a
 
 ## Books
 Some helpful books to formally introduce algorithims:
-* [Introduction to Algorithms (CLRS)](https://web.njit.edu/~wl256/download/cs610/Introduction-to-algorithm-3rdEdition.pdf) by Cormen, Leiserson, Rivest, Stein - Standard text for introductory algorithms courses; proof-based, very rigorous, comprehensive explanations.
+* [Introduction to Algorithms (CLRS)](https://web.njit.edu/~wl256/download/cs610/Introduction-to-algorithm-3rdEdition.pdf) by Cormen, Leiserson, Rivest, and Stein - Standard text for introductory algorithms courses; proof-based, very rigorous, comprehensive explanations.
 * [The Art of Computer Programming](https://www.amazon.com/Computer-Programming-Volumes-1-4A-Boxed/dp/0321751043) by Knuth - Extremely high level, incredibly detailed text; should be used primarily as a reference.
 * [Algorithms Unlocked](https://book-store.github.io/cse/thirdyear/[Thomas_H._Cormen]_Algorithms_unlocked(BookZZ.org).pdf) by Cormen - More accessible, beginner-friendly text; read this if you find CLRS too daunting.
 * [Algorithm Design](http://184.168.171.185/BOOKS/DVD%201/Algorithm%20Design%20-%20Jon%20Kleinberg,%20Eva%20Tardos.pdf) by Kleinberg and Tardos - Great introductory book, probably better suited for beginners than CLRS; great explanations of graph theoretic algorithms and analyses.
@@ -156,14 +156,46 @@ Some helpful books to formally introduce algorithims:
 * [Introduction to Algorithms: A Creative Approach](https://github.com/haseebr/competitive-programming/blob/master/Materials/Introduction%20to%20Algorithms%20by%20Udi%20Manber.pdf) by Manber - Very light, not as proof-heavy as industry standard texts like CLRS but still very informative; provides excellent algorithmic intuition.
 * [Algorithms](http://www.cse.iitd.ernet.in/~naveen/courses/CSL630/all.pdf) by Dasgupta, Papadimitriou, Vazirani -  Good, very readable, beginner-friendly algorithms textbook; used for decades at Berkeley and UCSD. 
 
+## Courses
+More courses avilable [here](https://github.com/prakhar1989/awesome-courses#algorithms)
+* [Algorithms](https://www.coursera.org/learn/algorithms-part1) on Coursera, taught by Wayne and Sedgewick (Princeton) - This online course goes hand in hand with Sedgewick's algorithms textbook, very good practical resource if you're using that book or you're familiar with Java.
+* [Algorithms](https://www.coursera.org/specializations/algorithms) on Coursera, taught by Roughgarden (Stanford) - More conceptual course, prioritizes mathematical analysis over implementation details.
+* [Introduction to Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005/video-lectures/) on MIT OCW, taught by Leiserson and Demaine (MIT) - This is a good counterpart to Cormen's text since it's taught by the L in CLRS; very rigorous, detailed course.
+* [Design and Analysis of Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/index.htm) on MIT OCW, taught by Demaine, Devadas, and Lynch (MIT) - Intermediate algorithms course; goes in-depth on a handful of topics/probelms, should only be taken after introductory courses.
+* [Data Structures in Java](https://www.coursera.org/specializations/java-object-oriented) on Coursera, taught by Porter, Alvarado, and Minnes (UCSD) - Good explanation of Java data structures including comparisons and performance analyses. 
+* [Advanced Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2008/) on MIT OCW, taught by Goemans (MIT) - Graduate level course, focuses largely on advanced data structures, graph theoretic algorithms, computational geometry, and number theoretic algorithms. Useful as a reference for exploring specific topics in-depth.
+* [Randomized Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002/index.htm) on MIT OCW, taught by Karger (MIT) - Graduate level course, explores randomized algorithms which aren't often covered comprehensively in many introductory algorithms texts/courses. Requires solid background in linear algebra, probabilty theory, and statistics. 
+* [Advanced Data Structures](http://courses.csail.mit.edu/6.851/spring12/lectures/) on MIT OCW, taught by Demaine (MIT) - Graduate level course, explores very advanced data structures and operations on them. 
+* [Advanced Algorithms](http://people.seas.harvard.edu/~minilek/cs224/fall14/hmwk.html) taught by Nelson (Harvard) - Very challenging problem sets; lectures assume broad knowledge about algorithms. 
+* [Introduction to Programming Contests](http://web.stanford.edu/class/cs97si/) taugth by Park (Stanford) - Good list of competitve programming algorithms and strategies for problem solving. Excellent resource for practice problems.
+
 ## Websites
 Websites that offer detailed explanations and complexity analyses for some of the above algorithims: 
-
-## Courses
+* [Topcoder Algorithms Tutorials](https://www.topcoder.com/community/data-science/data-science-tutorials/) - Phenomenal resource taught by competitive programming legends. Lots of challenging algorithms deconstructed and numerous applications demonstrated with Topcoder problems.
+* [GeeksforGeeks](http://www.geeksforgeeks.org) - Almost every well known algorithm is explained on this site with implementations, however some explanations are occasionally cursory/lacking in detail/unclear. 
+* [IOI Syllabus](https://people.ksp.sk/~misof/ioi-syllabus/) - Detailed list of topics covered by the IOI
+* [E-maxx](http://e-maxx-eng.appspot.com) - Translated from [Russian](http://e-maxx.ru/algo/) to English, very good list of useful algorithms and impelmentations along with explanations; widely used within the competitive programming community.
+* [Algorithms Discussion](http://codeforces.com/blog/entry/13529) - Codeforces blog post containing diverse array of algorithms (including less common techniques) and links to explanations/implementations.
+* [500 Data Structures and Algorithms](https://techiedelight.quora.com/500-Data-Structures-and-Algorithms-practice-problems-and-their-solutions) - Collection of software engineering interview questions and solutions.
 
 
 ## Online Judges
 Competitive programming websites and online judges which offer a plethora of interesting algorithmic problems for all skill levels:
+* [Topcoder](https://www.topcoder.com/my-dashboard) - Elite programming competitions; monetary prizes attract significant talent, makning this platform one of the most competitive. Very broad range of problems from beginner-friendly to extremely challenging. Challenge period avilable to win extra points by reading others' code and calling them out for incorrect solutions.  
+* [Codeforces](http://codeforces.com/contests) - On par with Topcoder, Codeforces offers longer, albeit similar problems, with similar styles. Only disadvantage is that competition times are often inconvenient if you live in the US. 
+* [Google Code Jam](https://code.google.com/codejam/) - Annual competition sponsored by Google, very fun problems (in my personal opinion).
+* [USACO]
+* [HackerRank]
+* [CodeChef](
+* [LeetCode](https://leetcode.com) - Offers light, fast interview-style problems rather than heavy algorithmic challenges. Excellent resource for preparing for software engineering interviews.
+* [HackerEarth](https://www.hackerearth.com) - Similar to LeetCode, not as competitive, offers fun 1v1 challenges.
+* [Project Euler](https://projecteuler.net) - Mathematical and number theoretic problems that require both mathematical insight and programming skill to solve; around 600 problems currently available. 
+* [Sphere Online Judge (SPOJ)](http://www.spoj.com) - Perhaps the best known online judge; very fast, accurate checkers and a diverse array of problems; diverse probelms for all levels; used by many competitive programmers to hone their skills.
+* [UVa Online Judge](https://uva.onlinejudge.org) - Very large database of contest problems including ACM-ICPC, geared towards more advanced programmers.
+* [Timus Online Judge](http://acm.timus.ru/?locale=en) - Russian online judge offering unique types of problems.
+* [Peking University Online Judge (POJ)](http://poj.org) - Used by Peking University ICPC team, good resource.
+* [Kattis](https://open.kattis.com) - Good, reliable online judge used by ACM-ICPC.
+
 
 ## Github Libraries
 (In case you don't speak Java)