Google Professional Machine Learning Engineer Certifications

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.