Udemy - Google Professional Machine Learning Engineer Certifications

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  • LanguageEnglish
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Google Professional Machine Learning Engineer Certifications

https://DevCourseWeb.com

Published 11/2024
Created by Google Cloud Experts Cloud Architect,Google Cloud Platform Gurus !
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 71 Lectures ( 10h 22m ) | Size: 3.43 GB

Detailed Machine Learning content from Beginners to Advanced to clear Professional ML Engineer certifications Exam

What you'll learn
Machine Learning Engineer on Cloud
Machine Learning Core Concepts
Foundations for AI
Google Cloud Machine Learning Services

Requirements
Knowledge of any programming or Python but not mandatory.

Files:

[ DevCourseWeb.com ] Udemy - Google Professional Machine Learning Engineer Certifications
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction to Machine Learning
    • 1 -Introduction.mp4 (30.9 MB)
    • 10 -Tensorboard.mp4 (44.3 MB)
    • 11 -Regularizations.mp4 (83.2 MB)
    • 12 -Regularizations Demo.mp4 (32.5 MB)
    • 13 -Feature Cross and One Hot Encoding.mp4 (66.7 MB)
    • 14 -Neural Networks (NN).mp4 (78.9 MB)
    • 15 -Embeddings.mp4 (126.4 MB)
    • 16 -TensorFlow Keras.mp4 (66.7 MB)
    • 17 -Sequential Model.mp4 (99.3 MB)
    • 18 -Model Save and Load for Serving.mp4 (56.3 MB)
    • 19 -Functional Model.mp4 (71.4 MB)
    • 2 -Bootstrapping Machine Learning.mp4 (34.4 MB)
    • 20 -Convolutional Neural Networks (CNN).mp4 (153.2 MB)
    • 21 -K-Nearest Neighbors algorithm (K-NN).mp4 (57.6 MB)
    • 22 -K-NN Classifier.mp4 (66.9 MB)
    • 23 -Decision Tree, Random Forest and Gini Index.mp4 (80.4 MB)
    • 24 -Boosting AdaBoost.mp4 (45.0 MB)
    • 25 -Other Concepts Ensemble, Boosting Bagging, Binning.mp4 (29.5 MB)
    • 3 -Lets Understand Machine Learning Core Concepts.mp4 (64.5 MB)
    • 4 -Machine Learning Types - Core 1.mp4 (48.4 MB)
    • 5 -Machine Learning Types - Core 2.mp4 (84.7 MB)
    • 6 -ML Engineer Section 00 - Chap 06 regression linear and logistics v01_D1.mp4 (33.0 MB)
    • 6 -Regression and Classification.mp4 (40.9 MB)
    • 7 -Training and Loss function.mp4 (49.5 MB)
    • 8 -Training and Loss function Precision and Recall.mp4 (79.0 MB)
    • 9 -Hyperparameter Tunning.mp4 (65.8 MB)
    10 - Section 6.0 Monitoring ML Solutions
    • 1 -Section 6 - Overview.mp4 (7.3 MB)
    • 2 -6.1 Identifying Risks to AI Solutions.mp4 (29.8 MB)
    • 3 -6.1 Model Explainability.mp4 (16.4 MB)
    • 4 -6.2 Monitoring, Testing, and Troubleshooting ML Solutions.mp4 (22.2 MB)
    2 - Introduction to Google Cloud Platform
    • 1 -Getting Started on Google Cloud Platform.mp4 (125.1 MB)
    • 2 -Google Cloud Platform Concepts.mp4 (76.9 MB)
    • 3 -Google Cloud Platform Concepts 2.mp4 (43.4 MB)
    • 4 -GCP - Compute Service.mp4 (46.5 MB)
    • 5 -GCP Database Service.mp4 (53.0 MB)
    • 6 -GCP Bigdata Services.mp4 (47.9 MB)
    • 7 -GCP Operations.mp4 (38.0 MB)
    • 8 -GCP Networks.mp4 (38.3 MB)
    • 9 -GCP Security.mp4 (28.9 MB)
    3 - Introduction to Machine Learning on GCP
    • 1 -Machine Learning on Google Cloud.mp4 (43.2 MB)
    • 2 -Vertex AI.mp4 (17.0 MB)
    • 3 -Datasets.mp4 (27.8 MB)
    • 4 -Feature Store.mp4 (22.5 MB)
    • 5 -AutoML.mp4 (125.6 MB)
    • 6 -Taking Sample ML Algorithms to GCP Part 1.mp4 (26.6 MB)
    • 7 -Training on GCP 2.mp4 (104.8 MB)
    • 8 -Training on GCP 3.mp4 (81.6 MB)
    4 - Googles Professional Machine Learning Engineer Certifications
    • 1 -Professional Machine Learning Engineer Certifications 2024.mp4 (10.7 MB)
    5 - Section 1 Architecting low-code ML solutions
    • 1 -Section 1 - Overview.mp4 (23.9 MB)
    • 2 -1.1 Developing ML models by using BigQuery ML.mp4 (54.7 MB)
    • 3 -BigQuery ML Create Model.mp4 (55.1 MB)
    • 4 -BigQuery ML Evaluate.mp4 (9.0 MB)
    • 5 -BigQuery ML Prediction.mp4 (10.2 MB)
    • 6 -BigQuery ML Feature Engineering.mp4 (33.1 MB)
    • 7 -Section 1.2 Building AI solutions by using ML APIs.mp4 (55.8 MB)
    • 8 -Section 1.2 Retail and Document AI.mp4 (43.9 MB)
    6 - Section 2 Collaborating within and across teams to manage data and Models
    • 1 -Section 2 - Overview.mp4 (21.3 MB)
    • 2 -2.1 Exploring and preprocessing organization-wide data.mp4 (43.0 MB)
    • 3 -2.2 Model Prototyping using Jupyter notebooks.mp4 (12.5 MB)
    • 4 -2.2 Security Best Practices.mp4 (46.0 MB)
    • 5 -2.3 Tracking and running ML experiments.mp4 (44.0 MB)
    7 - Section 3 Scaling prototypes into ML models
    • 1 -Section 3 - Overview.mp4 (9.2 MB)
    • 2 -3.1 Building Models - Frameworks.mp4 (3.6 MB)
    • 3 -3.2 Training Models.mp4 (18.5 MB)
    • 4 -3.3 Choosing Appropriate Hardware.mp4 (49.1 MB)
    • 5 -3.3 Distributed Training.mp4 (48.0 MB)
    8 - Section 4 Serving and Scaling Models
    • 1 -Section 4 - Overview.mp4 (13.9 MB)
    • 2 -4.1 Serving Model.mp4 (54.3 MB)
    • 3 -4.2 Scaling Online Model Serving.mp4 (58.4 MB)
    9 - Section 5 Automating and Orchestrating ML pipelines
    • 1 -Section 5 - Overview.mp4 (14.1 MB)
    • 2 -5.1 Developing end-to-end ML Pipelines.mp4 (47.7 MB)
    • 3 -5.2 Automating model Retraining.mp4 (23.6 MB)
    • Bonus Resources.txt (0.4 KB)

Code:

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