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Machine Learning Bootcamp • SVM Kmeans KNN LinReg PCA DBS (2022-04)

chesskom
Mar 18th, 2023
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  1. Machine Learning Bootcamp
  2. SVM • Kmeans • KNN • LinReg • PCA • DBS
  3. 4 sections • 69 lectures • 17h 45m total length
  4. 2022-04 | e-Learning | English | MP4, ZIP | 8.03 GB
  5.  
  6. Hands-on Machine Learning.
  7.  
  8.  
  9. DOWNLOAD:
  10. -------------------------
  11. https://wplik.com/uyjr07ubeysk/ml-bc-2022-04.part1.rar
  12. https://wplik.com/oe7x72sze2tv/ml-bc-2022-04.part2.rar
  13. https://wplik.com/7o2lm9vrtfva/ml-bc-2022-04.part3.rar
  14. https://wplik.com/63wt084zais3/ml-bc-2022-04.part4.rar
  15. -------------------------
  16. https://filerice.com/9sj2hmbauhuu/ml-bc-2022-04.part1.rar
  17. https://filerice.com/xk0q51j491ys/ml-bc-2022-04.part2.rar
  18. https://filerice.com/ndjb3zt0jo7l/ml-bc-2022-04.part3.rar
  19. https://filerice.com/04nbxk88bvh6/ml-bc-2022-04.part4.rar
  20. -------------------------
  21. https://rosefile.net/nau8k1b7kf/ml-bc-2022-04.part1.rar.html
  22. https://rosefile.net/3r318612id/ml-bc-2022-04.part2.rar.html
  23. https://rosefile.net/x3kh0udicm/ml-bc-2022-04.part3.rar.html
  24. https://rosefile.net/3mzjaf2lje/ml-bc-2022-04.part4.rar.html
  25. -------------------------
  26. https://rg.to/file/aa543fb1ccfe6dee3088c7c8d5788f92/ml-bc-2022-04.part1.rar
  27. https://rg.to/file/63bddef0513bd26d65418e3d4dff3e6a/ml-bc-2022-04.part2.rar
  28. https://rg.to/file/b0d8f96b1f80b653664a20e82039e481/ml-bc-2022-04.part3.rar
  29. https://rg.to/file/475bfe64a010d925b7fde654bfb8555a/ml-bc-2022-04.part4.rar
  30. -------------------------
  31. https://nitroflare.com/view/7A9A87293B81D36/ml-bc-2022-04.part1.rar
  32. https://nitroflare.com/view/A35C23ACD1CEE78/ml-bc-2022-04.part2.rar
  33. https://nitroflare.com/view/91D22173B84B56A/ml-bc-2022-04.part3.rar
  34. https://nitroflare.com/view/F8D45804BD463E7/ml-bc-2022-04.part4.rar
  35. -------------------------
  36.  
  37.  
  38.  
  39. What you'll learn
  40. - Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning.
  41.  
  42. Requirements
  43. - Simple programming knowledge is added advantage.
  44.  
  45. Description
  46.  
  47. The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.
  48.  
  49. UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.
  50.  
  51. Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.
  52.  
  53. UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.
  54.  
  55. The course contents are given below:
  56. - Introduction to Machine Learning
  57. - Introductions to Deep Learning
  58. - Installations
  59. - Unsupervised Learning
  60. - Clustering, Association
  61. - Agglomerative, Hands-on
  62. - PCA: Principal Component Analysis
  63. - DBSCAN, Hands-on
  64. - Mean Shift, Hands-on
  65. - K Means, Hands-on
  66. - Association Rules, Hands-on
  67. - Supervised Learning
  68. - Regression, Classification
  69. - Train Test Split, Hands-on
  70. - k Nearest Neighbors, Hands-on
  71. - kNN Algo Implementation
  72. - Support Vector Machine (SVM), Hands-on
  73. - Support Vector Regression (SVR), Hands-on
  74. - SVM (non linear svm params), Hands-on
  75. - SVM kernel trick, Hands-on
  76. - SVM mathematics
  77. - Linear Regression, Hands-on
  78. - Gradient Descent overview
  79. - One Hot Encoding (Dummy vars)
  80. - One Hot Encoding with Linear Regr, Hands-on
  81. - Naive Bayes Overview
  82. - Bayes' Concept, Hands-on
  83. - Naive Bayes' Classifier, Hands-on
  84. - Logistic Regression Overview
  85. - Binary Classification Logistic Regression
  86. - Multiclass Classification Logistic Regression
  87. - Decision Tree
  88. - ID3 Algorithm - Classifier
  89. - ID3 Algorithm - Regression
  90. - Info about Datasets
  91.  
  92. Who this course is for:
  93. - Python programmers, C/C++ programmers, working of scripting (like javascript), fresh developers and intermediate level programmers who want to learn Machine Learning.
  94.  
  95.  
  96.  
  97. MORE COURSES:
  98. -----------------------------------------
  99. https://rg.to/folder/6531533/WEBSITE.html
  100. ---------------------------------------------
  101. https://rg.to/folder/6432280/PROGRAMMING.html
  102. ------------------------------------------------
  103. https://rg.to/folder/5704524/ENGLISH_COURSE.html
  104. ------------------------------------------------
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