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[ CourseLala.com ] Skillshare - Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement

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CourseLala com Skillshare - Machine Learning Neural Networks Computer Vision Deep Learning and Reinforcement

文件列表
  1. ~Get Your Files Here !/01. Introduction.mp427.93MB
  2. ~Get Your Files Here !/02. Installing TensorFlow and Keras.mp46.23MB
  3. ~Get Your Files Here !/03. Why to learn artificial intelligence and machine learning.mp426.6MB
  4. ~Get Your Files Here !/04. Types of artificial intelligence learning.mp433.03MB
  5. ~Get Your Files Here !/05. Fundamentals of statistics.mp431.12MB
  6. ~Get Your Files Here !/06. What is linear regression.mp435.68MB
  7. ~Get Your Files Here !/07. Linear regression theory optimization.mp444.38MB
  8. ~Get Your Files Here !/08. Linear regression theory gradient descent.mp435MB
  9. ~Get Your Files Here !/09. Linear regression implementation I.mp487.21MB
  10. ~Get Your Files Here !/10. Linear regression implementation II.mp419.37MB
  11. ~Get Your Files Here !/11. What is logistic regression.mp438.17MB
  12. ~Get Your Files Here !/12. Logistic regression and maximum likelihood estimation.mp424.78MB
  13. ~Get Your Files Here !/13. Logistic regression example I sigmoid function.mp447.32MB
  14. ~Get Your Files Here !/14. Logistic regression example II credit scoring.mp466.5MB
  15. ~Get Your Files Here !/15. What is cross validation.mp426.48MB
  16. ~Get Your Files Here !/16. Cross validation example.mp426.1MB
  17. ~Get Your Files Here !/17. What is the k nearest neighbor classifier.mp416.67MB
  18. ~Get Your Files Here !/18. Concept of lazy learning.mp418.57MB
  19. ~Get Your Files Here !/19. Distance metrics Euclidean distance.mp420.79MB
  20. ~Get Your Files Here !/20. Bias and variance trade off.mp415.65MB
  21. ~Get Your Files Here !/21. K nearest neighbor implementation I.mp431.4MB
  22. ~Get Your Files Here !/22. K nearest neighbor implementation II.mp450.28MB
  23. ~Get Your Files Here !/23. K nearest neighbor implementation III.mp423.87MB
  24. ~Get Your Files Here !/24. What is the naive Bayes classifier.mp446.88MB
  25. ~Get Your Files Here !/25. Naive Bayes classifier illustration.mp49.69MB
  26. ~Get Your Files Here !/26. Naive Bayes classifier implementation.mp417.51MB
  27. ~Get Your Files Here !/27. What is text clustering.mp475.13MB
  28. ~Get Your Files Here !/28. Text clustering inverse document frequency (TF IDF).mp428.06MB
  29. ~Get Your Files Here !/29. Naive Bayes example clustering news.mp490.44MB
  30. ~Get Your Files Here !/30. What are Support Vector Machines (SVMs).mp421.27MB
  31. ~Get Your Files Here !/31. Linearly separable problems.mp434.56MB
  32. ~Get Your Files Here !/32. Non linearly separable problems.mp424.8MB
  33. ~Get Your Files Here !/33. Kernel functions.mp431.05MB
  34. ~Get Your Files Here !/34. Support vector machine example I simple.mp447.9MB
  35. ~Get Your Files Here !/35. Support vector machine example II iris dataset.mp432.35MB
  36. ~Get Your Files Here !/36. Support vector machines example III parameter tuning.mp441.6MB
  37. ~Get Your Files Here !/37. Support vector machine example IV digit recognition.mp442.81MB
  38. ~Get Your Files Here !/38. Support vector machine example V digit recognition.mp430.46MB
  39. ~Get Your Files Here !/39. Advantages and disadvantages.mp47.25MB
  40. ~Get Your Files Here !/40. Decision trees introduction basics.mp429.36MB
  41. ~Get Your Files Here !/41. Decision trees introduction entropy.mp440.5MB
  42. ~Get Your Files Here !/42. Decision trees introduction information gain.mp437.84MB
  43. ~Get Your Files Here !/43. The Gini index approach.mp432.21MB
  44. ~Get Your Files Here !/44. Decision trees introduction pros and cons.mp49.16MB
  45. ~Get Your Files Here !/45. Decision trees implementation I.mp429.56MB
  46. ~Get Your Files Here !/46. Decision trees implementation II parameter tuning.mp424.18MB
  47. ~Get Your Files Here !/47. Decision tree implementation III identifying cancer.mp430.33MB
  48. ~Get Your Files Here !/48. Pruning introduction.mp420.26MB
  49. ~Get Your Files Here !/49. Bagging introduction.mp428.19MB
  50. ~Get Your Files Here !/50. Random forest classifier introduction.mp420.81MB
  51. ~Get Your Files Here !/51. Random forests example I iris dataset.mp421.29MB
  52. ~Get Your Files Here !/52. Random forests example II credit scoring.mp414.47MB
  53. ~Get Your Files Here !/53. Random forests example III OCR parameter tuning.mp463.16MB
  54. ~Get Your Files Here !/54. Boosting introduction basics.mp419.44MB
  55. ~Get Your Files Here !/55. Boosting introduction illustration.mp415.46MB
  56. ~Get Your Files Here !/56. Boosting introduction equations.mp421.71MB
  57. ~Get Your Files Here !/57. Boosting introduction final formula.mp445.88MB
  58. ~Get Your Files Here !/58. Boosting implementation I iris dataset.mp430.59MB
  59. ~Get Your Files Here !/59. Boosting implementation II wine classification.mp476.66MB
  60. ~Get Your Files Here !/60. Boosting vs bagging.mp414.36MB
  61. ~Get Your Files Here !/61. Principal component analysis (PCA) introduction.mp438.65MB
  62. ~Get Your Files Here !/62. Principal component analysis example.mp449.95MB
  63. ~Get Your Files Here !/63. Principal component analysis example II.mp446.33MB
  64. ~Get Your Files Here !/64. K means clustering introduction.mp427.96MB
  65. ~Get Your Files Here !/65. K means clustering example.mp436.89MB
  66. ~Get Your Files Here !/66. K means clustering text clustering.mp465.81MB
  67. ~Get Your Files Here !/67. DBSCAN introduction.mp416.78MB
  68. ~Get Your Files Here !/68. DBSCAN example.mp442.34MB
  69. ~Get Your Files Here !/69. Hierarchical clustering introduction.mp417.45MB
  70. ~Get Your Files Here !/70. Hierarchical clustering example.mp436.55MB
  71. ~Get Your Files Here !/71. 008 Hierarchical clustering market segmentation.mp446.5MB
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