Introduction to Algorithms is a behemoth text book. I prefer O'Reilly's Algorithms in a Nutshell and also Programming Collective Intelligence" for basic ML stuff.

I'd grab beautifulsoup + scikit-learn + pandas from continum.io (they're part of the standard anaconda download), launch Spyder and follow through this:
http://sebastianraschka.com/Articles/2014_naive_bayes_1.html

You can get a RAKE impl here too : https://github.com/aneesha/RAKE

Doing recommendations on the web like that is covered in an accessible way in "Programming Collective Intelligence"

I've posted this before but I'll repost it here:

Now in terms of the question that you ask in the title - this is what I recommend:

Job Interview Prep

1. Cracking the Coding Interview: 189 Programming Questions and Solutions
   
2. Programming Interviews Exposed: Coding Your Way Through the Interview
   
3. Introduction to Algorithms
   
4. The Algorithm Design Manual
   
5. Effective Java
   
6. Concurrent Programming in Java™: Design Principles and Pattern
   
7. Modern Operating Systems
   
8. Programming Pearls
   
9. Discrete Mathematics for Computer Scientists
   
   

   Junior Software Engineer Reading List
   

   
   

   Read This First
   

   
   

10. Pragmatic Thinking and Learning: Refactor Your Wetware
    
    

    Fundementals
    

    
    

11. Code Complete: A Practical Handbook of Software Construction
    
12. Software Estimation: Demystifying the Black Art
    
13. Software Engineering: A Practitioner's Approach
    
14. Refactoring: Improving the Design of Existing Code
    
15. Coder to Developer: Tools and Strategies for Delivering Your Software
    
16. Perfect Software: And Other Illusions about Testing
    
17. Getting Real: The Smarter, Faster, Easier Way to Build a Successful Web Application
    
    

    Understanding Professional Software Environments
    

    
    

18. Agile Software Development: The Cooperative Game
    
19. Software Project Survival Guide
    
20. The Best Software Writing I: Selected and Introduced by Joel Spolsky
    
21. Debugging the Development Process: Practical Strategies for Staying Focused, Hitting Ship Dates, and Building Solid Teams
    
22. Rapid Development: Taming Wild Software Schedules
    
23. Peopleware: Productive Projects and Teams
    
    

    Mentality
    

    
    

24. Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency
    
25. Against Method
    
26. The Passionate Programmer: Creating a Remarkable Career in Software Development
    
    

    History
    

    
    

27. The Mythical Man-Month: Essays on Software Engineering
    
28. Computing Calamities: Lessons Learned from Products, Projects, and Companies That Failed
    
29. The Deadline: A Novel About Project Management
    
    

    Mid Level Software Engineer Reading List
    

    
    

    Read This First
    

    
    

30. Personal Development for Smart People: The Conscious Pursuit of Personal Growth
    
    

    Fundementals
    

    
    

31. The Clean Coder: A Code of Conduct for Professional Programmers
    
32. Clean Code: A Handbook of Agile Software Craftsmanship
    
33. Solid Code
    
34. Code Craft: The Practice of Writing Excellent Code
    
35. Software Craftsmanship: The New Imperative
    
36. Writing Solid Code
    
    

    Software Design
    

    
    

37. Head First Design Patterns: A Brain-Friendly Guide
    
38. Design Patterns: Elements of Reusable Object-Oriented Software
    
39. Domain-Driven Design: Tackling Complexity in the Heart of Software
    
40. Domain-Driven Design Distilled
    
41. Design Patterns Explained: A New Perspective on Object-Oriented Design
    
42. Design Patterns in C# - Even though this is specific to C# the pattern can be used in any OO language.
    
43. Refactoring to Patterns
    
    

    Software Engineering Skill Sets
    

    
    

44. Building Microservices: Designing Fine-Grained Systems
    
45. Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
    
46. NoEstimates: How To Measure Project Progress Without Estimating
    
47. Object-Oriented Software Construction
    
48. The Art of Software Testing
    
49. Release It!: Design and Deploy Production-Ready Software
    
50. Working Effectively with Legacy Code
    
51. Test Driven Development: By Example
    
    

    Databases
    

    
    

52. Database System Concepts
    
53. Database Management Systems
    
54. Foundation for Object / Relational Databases: The Third Manifesto
    
55. Refactoring Databases: Evolutionary Database Design
    
56. Data Access Patterns: Database Interactions in Object-Oriented Applications
    
    

    User Experience
    

    
    

57. Don't Make Me Think: A Common Sense Approach to Web Usability
    
58. The Design of Everyday Things
    
59. Programming Collective Intelligence: Building Smart Web 2.0 Applications
    
60. User Interface Design for Programmers
    
61. GUI Bloopers 2.0: Common User Interface Design Don'ts and Dos
    
    

    Mentality
    

    
    

62. The Productive Programmer
    
63. Extreme Programming Explained: Embrace Change
    
64. Coders at Work: Reflections on the Craft of Programming
    
65. Facts and Fallacies of Software Engineering
    
    

    History
    

    
    

66. Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software
    
67. New Turning Omnibus: 66 Excursions in Computer Science
    
68. Hacker's Delight
    
69. The Alchemist
    
70. Masterminds of Programming: Conversations with the Creators of Major Programming Languages
    
71. The Information: A History, A Theory, A Flood
    
    

    Specialist Skills
    

    
    In spite of the fact that many of these won't apply to your specific job I still recommend reading them for the insight, they'll give you into programming language and technology design.
    
    

72. Peter Norton's Assembly Language Book for the IBM PC
    
73. Expert C Programming: Deep C Secrets
    
74. Enough Rope to Shoot Yourself in the Foot: Rules for C and C++ Programming
    
75. The C++ Programming Language
    
76. Effective C++: 55 Specific Ways to Improve Your Programs and Designs
    
77. More Effective C++: 35 New Ways to Improve Your Programs and Designs
    
78. More Effective C#: 50 Specific Ways to Improve Your C#
    
79. CLR via C#
    
80. Mr. Bunny's Big Cup o' Java
    
81. Thinking in Java
    
82. JUnit in Action
    
83. Functional Programming in Scala
    
84. The Art of Prolog: Advanced Programming Techniques
    
85. The Craft of Prolog
    
86. Programming Perl: Unmatched Power for Text Processing and Scripting
    
87. Dive into Python 3
    
88. why's (poignant) guide to Ruby

In addition to BeautifulSoup there's also Scrapy if you want to do some crawling and screen scraping. http://doc.scrapy.org/en/latest/intro/overview.html

You might consider this book for a starter into data mining and machine learning. It uses Python for the code samples.

http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325

I liked Machine Learning For Hackers, Programming Collective Intelligence and The Elements of Statistical Learning.

Seconding collaborative filtering. It's also a fairly simple algorithm to implement yourself as long as you're not using Wikipedia as a guide.

Collaborative filtering is like what Amazon uses to figure out what products to recommend to its users. It finds users that have similar purchasing habits to yourself and recommends items that they bought.

The first chapters of Programming Collective Intelligence describe how to implement Collaborative Filtering in Python in a really intuitive way, along with providing source code. Two hours in and you'll have a working service recommendation system. I'd definitely recommend that book to anyone looking to build what OP is interested in making.

If you want a quick non-textbook to get your feet wet, Oreilly's Programming Collective Intelligence isn't half bad.

Programming Collective Intelligence - but it uses python.

O'Reilly has published a number of practical machine learning books such as Programming Collective Intelligence: Building Smart Web 2.0 Applications and Natural Language Processing with Python that you might find good starting points.

Also machine learning, you profile sounds pretty good for machine learning. Do check out Andew Ng's videos, and this book. Machine learning is very much in demand right now, from AI, computational biology, finance, there's hardly any area where it isn't being used.

I swear by this book for an introduction to GAs and a ton of other cool ML/AI algorithms. No advanced math/probability knowledge necessary; it's focused on practical examples and intuitive explanations. It's an excellent foundation for further study.

Collective Intelligence

Sounds like you're running into O(n^2) or O(n^3) blowup. You didn't describe what algorithm you're using. Which probably means you don't know it, which means you don't know what the complexity is.

You need to make an index by item recommended. For speed, do it in C++ (e.g. a simple hash_map), but Python will be good to play with the algorithm.

Try posting 1M rows and I bet someone here (including I) could write something simple quite quickly.

Also try: http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325

Although I don't believe they directly addressed algorithmic complexity. They presented some n^2 algorithms without really saying so.

FWIW , You might enjoy Programming Collective Intelligence if you liked this talk.

link to buy off author's website

I'm a general engineer myself, with a side interest in computer science. Szeliski's book is probably the big one in the computer vision field. Another you might be interested in is Computer Vision by Linda Shapiro.

You may also be interested in machine learning in general, for which I can give you two books:

• Heavily Theoretical: Christopher Bishop - Pattern Recognition and Machine Learning (Very much research oriented. Assumes you're decent at calculus.)
  
• Heavily Practical: Toby Segaran - Programming Collective Intelligence (A shallow look at the most common practical algorithms straightforwardly applied to real life examples.)
  
  I see you're interested in compilers. The Dragon book mainly focuses on parsing algorithms. I found learning about Forth implementations to be very instructive when learning about code generation. jonesforth is a good one if you understand x86 assembly.

I figure a business background and are looking to incorporate machine learning/AI into your pipeline. Programming Collective Intelligence: Building Smart Web 2.0 Applications is a must-read. Doesn't go too much into it but still gives you a good idea of the popular ML techniques and how they're being used by top companies.

If you want to learn the algorithms by programming them you have Programming Collective Intelligence that is really good. It really helped me to see the algorithms in work in order to deeply understand them.

Here are some links for the product in the above comment for different countries:

Amazon Smile Link: this book


|Country|Link|
|:-----------|:------------|
|UK|amazon.co.uk|
|Spain|amazon.es|
|France|amazon.fr|
|Germany|amazon.de|
|Japan|amazon.co.jp|
|Canada|amazon.ca|
|Italy|amazon.it|
|China|amazon.cn|




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