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Learning Path: Machine Learning

Video Description

With the growing prominence of data science and its uses across all types of business, it's the perfect time to start applying machine learning. In this Learning Path, you'll master everything you need to transform data into action. Start with basic techniques and move on to coding your own machine learning algorithms.

Table of Contents

  1. An Introduction to Machine Learning with Web Data, by Hilary Mason
    1. Introduction 00:20:25
    2. Classifying Web Documents - The Theory 00:29:23
    3. Classifying Web Documents - The Code 00:47:57
    4. Clustering, Recommendations, and Probability 00:54:07
    5. Conclusion 00:11:30
  2. Advanced Machine Learning, by Hilary Mason
    1. Introduction 00:19:18
    2. Classification Part 1 00:18:05
    3. Classification Part 2 00:42:22
    4. Clustering 00:26:10
    5. Learning From Data 00:21:16
    6. Conclusions 00:06:43
  3. Deep Learning
    1. Introduction - Deep Learning at Strata+Hadoop World - Ben Lorica 00:01:06
    2. Neural Networks for Machine Perception - Ilya Sutskever 00:29:58
    3. Beyond DNNs towards New Architectures for Deep Learning, with Applications to Large Vocabulary Continuous Speech Recognition - Tara Sainath 00:34:05
    4. A Quest for Visual Intelligence in Computers - Fei-Fei Li 00:29:39
    5. Building and Deploying Large-scale Machine Learning Pipelines Using the Berkeley Data Analytics Stack - Ben Recht 00:28:04
  4. Learning Data Structures and Algorithms, by Rod Stephens
    1. Introduction And Course Overview 00:04:01
    2. About The Author 00:01:16
    3. How To Access Your Working Files 00:01:15
    4. Complexity Theory 00:03:56
    5. Big O Notation 00:07:03
    6. Typical Runtime Functions 00:04:37
    7. Comparing Runtime Functions 00:05:27
    8. P And NP 00:04:04
    9. Random Numbers 00:02:19
    10. Linear Congruential Generators 00:05:04
    11. Randomizing Arrays - Part 1 00:03:47
    12. Randomizing Arrays - Part 2 00:04:31
    13. GCD 00:04:09
    14. LCM 00:03:29
    15. Prime Factorization - Part 1 00:04:59
    16. Prime Factorization - Part 2 00:02:43
    17. Finding Primes 00:03:24
    18. Testing Primality 00:03:46
    19. Numerical Integration 00:05:11
    20. Singly Linked Lists - Part 1 00:06:48
    21. Singly Linked Lists - Part 2 00:02:23
    22. Sorted Linked Lists 00:03:23
    23. Sorting With Linked Lists 00:04:08
    24. Doubly Linked Lists 00:03:52
    25. One-Dimensional Arrays 00:05:10
    26. Triangular Arrays - Part 1 00:04:13
    27. Triangular Arrays - Part 2 00:03:18
    28. Sparse Arrays - Part 1 00:05:28
    29. Sparse Arrays - Part 2 00:03:20
    30. Stacks 00:02:33
    31. Stack Algorithms 00:03:27
    32. Double Stacks 00:02:09
    33. Queues 00:05:49
    34. Sorting Algorithms 00:03:04
    35. Insertionsort 00:06:28
    36. Selectionsort 00:04:47
    37. Quicksort - Part 1 00:05:40
    38. Quicksort - Part 2 00:07:55
    39. Heapsort - Part 1 00:06:17
    40. Heapsort - Part 2 00:05:21
    41. Heapsort - Part 3 00:05:40
    42. Mergesort - Part 1 00:03:56
    43. Mergesort - Part 2 00:03:41
    44. Bubblesort - Part 1 00:04:51
    45. Bubblesort - Part 2 00:04:18
    46. Countingsort - Part 1 00:04:46
    47. Countingsort - Part 2 00:03:35
    48. Sorting Summary 00:02:51
    49. Linear Search 00:02:12
    50. Binary Search 00:05:15
    51. Interpolation Search 00:05:27
    52. Hash Tables 00:04:33
    53. Chaining 00:05:24
    54. Open Addressing - Basics 00:07:26
    55. Open Addressing - Linear Probing 00:04:48
    56. Open Addressing - Quadratic Probing 00:04:23
    57. Open Addressing - Double Hashing 00:05:56
    58. Recursion Basics 00:05:37
    59. Fibonacci Numbers 00:06:08
    60. Tower Of Hanoi 00:06:08
    61. Koch Curves 00:04:32
    62. Hilbert Curves 00:04:32
    63. Gaskets 00:04:52
    64. Removing Tail Recursion 00:03:59
    65. Removing Recursion With Stacks 00:03:56
    66. Fixing Fibonacci 00:07:25
    67. Selections 00:04:16
    68. Permutations 00:04:12
    69. Backtracking 00:06:04
    70. The Eight Queens Problem - Part 1 00:06:01
    71. The Eight Queens Problem - Part 2 00:04:04
    72. The Eight Queens Problem - Part 3 00:03:49
    73. The Knights Tour 00:04:21
    74. Tree Terms 00:05:06
    75. Binary Tree Properties 00:06:25
    76. Traversals - Preorder 00:03:55
    77. Traversals - Postorder 00:02:57
    78. Traversals - Inorder 00:02:48
    79. Traversals - Breadth-First 00:02:58
    80. Building Sorted Trees 00:03:56
    81. Editing Sorted Trees 00:04:36
    82. Why Do You Need Balanced Trees? 00:04:04
    83. B-Trees - B-Tree Basics 00:07:19
    84. B-Trees - Adding Items 00:05:16
    85. B-Trees - Removing Items 00:04:16
    86. Definition 00:05:37
    87. Exhaustive Search 00:06:27
    88. Branch And Bound 00:08:26
    89. Heuristics 00:07:38
    90. Network Terminology 00:03:32
    91. Network Classes 00:04:53
    92. Depth-First Traversal 00:05:21
    93. Breadth-First Traversal 00:02:44
    94. Spanning Trees - Part 1 00:04:13
    95. Spanning Trees - Part 2 00:03:58
    96. Shortest Paths - Part 1 00:07:27
    97. Shortest Paths - Part 2 00:08:42
    98. Wrap-Up 00:01:17
  5. Hardcore Data Science NYC 2014
    1. Introduction - Hardcore Data Science NYC 2014 - Ben Lorica 00:01:07
    2. Doing the Impossible (Almost) - Ted Dunning 00:24:22
    3. Tupleware: Redefining Modern Analytics - Tim Kraska 00:29:05
    4. Data Science for Humans, Not Robots - Alice Zheng 00:22:39
    5. Big Data: Efficient Collection and Processing - Anna Gilbert 00:42:22
    6. Computational Problems in Managing Social Information - Jon Kleinberg 00:51:26
    7. Small Data Problems - Kira Radinsky 00:23:26
    8. Learning About Music and Listeners - Brian Whitman 00:29:59
    9. Statistical Topic Modeling - Hanna Wallach 00:28:48
    10. The Aha! Moment: From Data to Insight - Dafna Shahaf 00:26:32
  6. Hardcore Data Science California 2015
    1. Introduction - Hardcore Data Science California 2015 - Ben Lorica 00:01:07
    2. On the Computational and Statistical Interface and "Big Data" - Michael Jordan 00:48:48
    3. Interpretable Machine Learning in Practice - Maya Gupta 00:26:24
    4. Visual Understanding Beyond Naming - Alyosha Efros 00:36:31
    5. Finding Repeated Structure in Time Series Data: Commercial and Scientific Opportunities - Eamonn Keogh 00:21:25
    6. Tensor Methods for Large-scale Unsupervised Learning: Applications to Topic and Community Modeling - Anima Anandkumar 00:31:09
    7. High Performance Machine Learning through Codesign and Rooflining - John Canny 00:31:10
    8. Graph Mining for Log Data - David Andrzejewski 00:27:59
    9. Why Julia's Important for Data Science - John Myles White 00:24:37
    10. Drugs, DNA, and Dinosaurs: Building High Quality Knowledge Bases with DeepDive - Chris Re 00:30:20