Google Professional Machine Learning Engineer Certifications DevCourseWeb

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Files
  1. Get Bonus Downloads Here.url182 bytes
  2. 1 -Introduction.mp430.9 MB
  3. 10 -Tensorboard.mp444.3 MB
  4. 11 -Regularizations.mp483.2 MB
  5. 12 -Regularizations Demo.mp432.5 MB
  6. 13 -Feature Cross and One Hot Encoding.mp466.7 MB
  7. 14 -Neural Networks (NN).mp478.9 MB
  8. 15 -Embeddings.mp4126.4 MB
  9. 16 -TensorFlow Keras.mp466.7 MB
  10. 17 -Sequential Model.mp499.3 MB
  11. 18 -Model Save and Load for Serving.mp456.3 MB
  12. 19 -Functional Model.mp471.4 MB
  13. 2 -Bootstrapping Machine Learning.mp434.4 MB
  14. 20 -Convolutional Neural Networks (CNN).mp4153.2 MB
  15. 21 -K-Nearest Neighbors algorithm (K-NN).mp457.6 MB
  16. 22 -K-NN Classifier.mp466.9 MB
  17. 23 -Decision Tree, Random Forest and Gini Index.mp480.4 MB
  18. 24 -Boosting AdaBoost.mp445.0 MB
  19. 25 -Other Concepts Ensemble, Boosting Bagging, Binning.mp429.5 MB
  20. 3 -Lets Understand Machine Learning Core Concepts.mp464.5 MB
  21. 4 -Machine Learning Types - Core 1.mp448.4 MB
  22. 5 -Machine Learning Types - Core 2.mp484.7 MB
  23. 6 -ML Engineer Section 00 - Chap 06 regression linear and logistics v01_D1.mp433.0 MB
  24. 6 -Regression and Classification.mp440.9 MB
  25. 7 -Training and Loss function.mp449.5 MB
  26. 8 -Training and Loss function Precision and Recall.mp479.0 MB
  27. 9 -Hyperparameter Tunning.mp465.8 MB
  28. 1 -Section 6 - Overview.mp47.3 MB
  29. 2 -6.1 Identifying Risks to AI Solutions.mp429.8 MB
  30. 3 -6.1 Model Explainability.mp416.4 MB
  31. 4 -6.2 Monitoring, Testing, and Troubleshooting ML Solutions.mp422.2 MB
  32. 1 -Getting Started on Google Cloud Platform.mp4125.1 MB
  33. 2 -Google Cloud Platform Concepts.mp476.9 MB
  34. 3 -Google Cloud Platform Concepts 2.mp443.4 MB
  35. 4 -GCP - Compute Service.mp446.5 MB
  36. 5 -GCP Database Service.mp453.0 MB
  37. 6 -GCP Bigdata Services.mp447.9 MB
  38. 7 -GCP Operations.mp438.0 MB
  39. 8 -GCP Networks.mp438.3 MB
  40. 9 -GCP Security.mp428.9 MB
  41. 1 -Machine Learning on Google Cloud.mp443.2 MB
  42. 2 -Vertex AI.mp417.0 MB
  43. 3 -Datasets.mp427.8 MB
  44. 4 -Feature Store.mp422.5 MB
  45. 5 -AutoML.mp4125.6 MB
  46. 6 -Taking Sample ML Algorithms to GCP Part 1.mp426.6 MB
  47. 7 -Training on GCP 2.mp4104.8 MB
  48. 8 -Training on GCP 3.mp481.6 MB
  49. 1 -Professional Machine Learning Engineer Certifications 2024.mp410.7 MB
  50. 1 -Section 1 - Overview.mp423.9 MB
  51. 2 -1.1 Developing ML models by using BigQuery ML.mp454.7 MB
  52. 3 -BigQuery ML Create Model.mp455.1 MB
  53. 4 -BigQuery ML Evaluate.mp49.0 MB
  54. 5 -BigQuery ML Prediction.mp410.2 MB
  55. 6 -BigQuery ML Feature Engineering.mp433.1 MB
  56. 7 -Section 1.2 Building AI solutions by using ML APIs.mp455.8 MB
  57. 8 -Section 1.2 Retail and Document AI.mp443.9 MB
  58. 1 -Section 2 - Overview.mp421.3 MB
  59. 2 -2.1 Exploring and preprocessing organization-wide data.mp443.0 MB
  60. 3 -2.2 Model Prototyping using Jupyter notebooks.mp412.5 MB
  61. 4 -2.2 Security Best Practices.mp446.0 MB
  62. 5 -2.3 Tracking and running ML experiments.mp444.0 MB
  63. 1 -Section 3 - Overview.mp49.2 MB
  64. 2 -3.1 Building Models - Frameworks.mp43.6 MB
  65. 3 -3.2 Training Models.mp418.5 MB
  66. 4 -3.3 Choosing Appropriate Hardware.mp449.1 MB
  67. 5 -3.3 Distributed Training.mp448.0 MB
  68. 1 -Section 4 - Overview.mp413.9 MB
  69. 2 -4.1 Serving Model.mp454.3 MB
  70. 3 -4.2 Scaling Online Model Serving.mp458.4 MB
  71. 1 -Section 5 - Overview.mp414.1 MB
  72. 2 -5.1 Developing end-to-end ML Pipelines.mp447.7 MB
  73. 3 -5.2 Automating model Retraining.mp423.6 MB
  74. Bonus Resources.txt386 bytes

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