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Google Professional Machine Learning Engineer Certifications
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.44 GB | Duration: 10h 22m
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.
Description
Greetings Machine Learning Lerner's ! We have 450,000+ Subscriptions & 323,000 Unique Students for Google Cloud Platform Certifications making us "No 1 Training for Google Cloud Platform on Udemy" The structure of this course- Aligns exact syllabus to training materials (final section is still under progress)- Detail theory as well as demos - Syllabus coverage Analysis for every sectionCovers Introduction to ML and Course syllabus so that you don't have to look anywhere else. Introduction to ML Introduction to GCPML on GCP Certification DetailsRemaining sections are one-to-one mapping with Google certification outline for Certification -> Professional Machine Learning Certifications covers all sections in exam Architect low-code AI solutionsCollaborate within and across teams to manage data and modelsScale prototypes into ML modelsServe and scale modelsAutomate and orchestrate ML pipelinesMonitor AI solutionsThe majority of IT professionals around the world hold at least one certification. The Global Knowledge 2024 IT Skills and Salary Report found that 85% of IT professionals hold at least one certification and that 66% of these professionals intend to acquire a new certification this year.Udemy's Lifetime Availability Guarantee - If you purchase ONCE, you will receive a lifetime update for Google Cloud Platform Certifications. Thank You GCP Gurus
Overview
Section 1: Introduction to Machine Learning
Lecture 1 Introduction
Lecture 2 Bootstrapping Machine Learning
Lecture 3 Lets Understand Machine Learning Core Concepts
Lecture 4 Machine Learning Types - Core 1
Lecture 5 Machine Learning Types - Core 2
Lecture 6 Regression and Classification
Lecture 7 Training and Loss function
Lecture 8 Training and Loss function Precision and Recall.
Lecture 9 Hyperparameter Tunning
Lecture 10 Tensorboard
Lecture 11 Regularizations
Lecture 12 Regularizations Demo
Lecture 13 Feature Cross and One Hot Encoding
Lecture 14 Neural Networks (NN)
Lecture 15 Embeddings
Lecture 16 TensorFlow Keras
Lecture 17 Sequential Model
Lecture 18 Model Save and Load for Serving
Lecture 19 Functional Model
Lecture 20 Convolutional Neural Networks (CNN)
Lecture 21 K-Nearest Neighbors algorithm (K-NN)
Lecture 22 K-NN Classifier
Lecture 23 Decision Tree, Random Forest and Gini Index
Lecture 24 Boosting AdaBoost
Lecture 25 Other Concepts : Ensemble, Boosting Bagging, Binning
Section 2: Introduction to Google Cloud Platform
Lecture 26 Getting Started on Google Cloud Platform
Lecture 27 Google Cloud Platform Concepts
Lecture 28 Google Cloud Platform Concepts 2
Lecture 29 GCP - Compute Service
Lecture 30 GCP Database Service
Lecture 31 GCP Bigdata Services
Lecture 32 GCP Operations
Lecture 33 GCP Networks
Lecture 34 GCP Security
Section 3: Introduction to Machine Learning on GCP
Lecture 35 Machine Learning on Google Cloud
Lecture 36 Vertex AI
Lecture 37 Datasets
Lecture 38 Feature Store
Lecture 39 AutoML
Lecture 40 Taking Sample ML Algorithms to GCP Part 1
Lecture 41 Training on GCP 2
Lecture 42 Training on GCP 3
Section 4: Googles Professional Machine Learning Engineer Certifications
Lecture 43 Professional Machine Learning Engineer Certifications 2024
Section 5: Section 1: Architecting low-code ML solutions
Lecture 44 Section 1 - Overview
Lecture 45 1.1 Developing ML models by using BigQuery ML
Lecture 46 BigQuery ML : Create Model
Lecture 47 BigQuery ML : Evaluate
Lecture 48 BigQuery ML : Prediction
Lecture 49 BigQuery ML : Feature Engineering
Lecture 50 Section 1.2 Building AI solutions by using ML APIs
Lecture 51 Section 1.2 Retail and Document AI
Section 6: Section 2: Collaborating within and across teams to manage data and Models
Lecture 52 Section 2 - Overview
Lecture 53 2.1 Exploring and preprocessing organization-wide data
Lecture 54 2.2 Model Prototyping using Jupyter notebooks
Lecture 55 2.2 Security Best Practices
Lecture 56 2.3 Tracking and running ML experiments
Section 7: Section 3: Scaling prototypes into ML models
Lecture 57 Section 3 - Overview
Lecture 58 3.1 Building Models - Frameworks
Lecture 59 3.2 Training Models
Lecture 60 3.3 Choosing Appropriate Hardware
Lecture 61 3.3 Distributed Training
Section 8: Section 4: Serving and Scaling Models
Lecture 62 Section 4 - Overview
Lecture 63 4.1 Serving Model
Lecture 64 4.2 Scaling Online Model Serving
Section 9: Section 5: Automating and Orchestrating ML pipelines
Lecture 65 Section 5 - Overview
Lecture 66 5.1 Developing end-to-end ML Pipelines
Lecture 67 5.2 Automating model Retraining
Section 10: Section 6.0 Monitoring ML Solutions
Lecture 68 Section 6 - Overview
Lecture 69 6.1 Identifying Risks to AI Solutions
Lecture 70 6.1 Model Explainability
Lecture 71 6.2 Monitoring, Testing, and Troubleshooting ML Solutions
Beginners Machine Learning who wants to start using ML on GCP,Beginners Cloud Data Engineers,Beginners Cloud Developers.
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