CourseClub Me MANNING - Machine Learning for Mere Mortals

1. U01M01 The basics.mp434.68MB
2. U01M02 Machine Learning versus Artificial Intelligence.mp433.01MB
3. U01M03 Supervised learning.mp443.06MB
4. U01M04 Unsupervised learning.mp416.22MB
5. U01M05 Reinforcement learning.mp428.4MB
6. U01M06 A quick math refresher.mp411.02MB
7. U01M07 Slope of a line.mp445.1MB
8. U01M08 Scalars, vectors, and tensors.mp426.47MB
9. U01M09 Matrices and matrix arithmetic.mp413.44MB
10. U01M10 Set up your computing environment.mp41.83MB
11. U01M11 Install Python tools.mp411.18MB
12. U01M12 Create virtualenv environment.mp45.88MB
13. U01M13 Install Tensorflow.mp418.31MB
14. U01M14 The projects.mp410.06MB
15. U02M01 Supervised learning.mp422.32MB
16. U02M02 Trend lines.mp44.62MB
17. U02M03 Cost functions.mp43.8MB
18. U02M04 Minimizing cost functions.mp48.78MB
19. U02M05 Visualizing data.mp434.97MB
20. U02M06 Using linear regression to predict values.mp422.84MB
21. U02M07 More complicated functions.mp41.71MB
22. U02M08 Working with matrices.mp44.61MB
23. U02M09 Letting Tensorflow do the hard work.mp414.17MB
24. U03M01 More supervised learning.mp44.72MB
25. U03M02 What are features_.mp45.63MB
26. U03M03 What makes a good feature_.mp415.53MB
27. U03M04 Decision trees.mp410.58MB
28. U03M05 K-nearest neighbor.mp49.92MB
29. U03M06 Linear classification.mp47.89MB
30. U03M07 Making it work in Tensorflow.mp421.22MB
31. U03M08 Creating a spam filter.mp413.66MB
32. U03M09 Tools and data for email classification.mp436MB
33. U03M10 Classifying emails.mp49.15MB
34. U04M01 How clustering works.mp482.12MB
35. U04M02 Clustering algorithms.mp456.82MB
36. U04M03 Introducing k-means.mp456.87MB
37. U04M06 Assigning Points to a Centroid in K-means).mp434.33MB
38. U05M01 What are neural networks, and how do they work.mp468.7MB
39. U05M02 The Tensorflow Playground interface.mp414.04MB
40. U05M03 Adding nodes to use multiple models in the TensorFlow Playground.mp412.81MB
41. U05M04 What hidden layers are, and how to use them with TensorFlow Playground.mp443.92MB
42. U05M05 What is the activation function in a neural network_.mp435.82MB
43. U06M01 Using Neural Networks.mp456.56MB
44. U06M02 How encoding non-numeric data works.mp464.84MB
45. U06M03 One hot encoding.mp484.5MB
46. U06M04 How image recognition relates to a neural network.mp495.14MB
47. U07M01 Encoding and Representation.mp421.27MB
48. U07M02 Numeric representation of data.mp440.61MB
49. U07M03 Text representation of data.mp431.82MB
50. U07M04 Representation of image data.mp434.06MB
51. U07M05 Representation of audio data.mp428.39MB
52. U07M06 Analytics, stock prices, and other time series data.mp443.63MB
53. U07M07 Preparing data_ finding the data set.mp431.31MB
54. U07M08 Preparing data_ Features engineering.mp461.38MB
55. U07M09 Principal Component Analysis_ The mathematical way to determine features.mp423.11MB
56. U07M10 Feature selection.mp434.54MB
57. U07M11 Geometry of the data space and the curse of dimensionality.mp458.61MB
58. U08M01 The difference between an algorithm and a model.mp450.27MB
59. U08M02 Chaining together models.mp4146.62MB
60. U09M01 Improving performance in machine learning routines.mp489.81MB
61. U09M02 Using parallelization.mp457.25MB
62. U09M03 Outliers.mp4204.64MB
63. U09M05 What should we do with outliers_.mp4119.1MB
64. U09M06 Robustness and noise.mp440.25MB
65. U09M07 Overfitting.mp4104.22MB
66. U09M08 Regularization.mp4272.46MB