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https://github.com/galois1915/google-ml-engineer

This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.
https://github.com/galois1915/google-ml-engineer

api automl bigquery keras mlops-workflow tensorflow2 vertex-ai

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This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.

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README 

        
# Machine Learning Engineer Professional Certificate

* Learn the skills needed to be successful in a machine learning engineering role

* Prepare for the Google Cloud Professional Machine Learning Engineer certification exam

* Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies

* Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications



## Introduction to AI and Machine Learning on Google cloud

This course, "Introduction to AI and Machine Learning on Google Cloud," provides a comprehensive overview of AI technologies and **tools offered by Google**. The course is organized into different layers, starting with the AI foundation layer where you learn about **cloud essentials and data tools**. Then, you explore different options to build machine learning projects in the AI development layer, including **out-of-the-box solutions, low code or no code options, and do-it-yourself** approaches. You also learn how to train and serve machine learning models using **Vertex AI**, Google Cloud's AI development platform. Finally, you are introduced to **generative AI** and how it empowers AI development and solutions. By the end of the course, you will be able to recognize the data to AI technologies and tools offered by Google Cloud, leverage generative AI capabilities, choose between different options to develop an AI project on Google Cloud, and build machine learning models end-to-end using Vertex AI.





  



Coursera 8BEVRID92B5E-1



## Launching into Machine Learning

This course, "Launching into Machine Learning," provides foundational knowledge in machine learning. Throughout the course, you will learn about improving **data quality, performing exploratory data analysis, building and training ML models using Vertex AI, AutoML and BigQuery ML, optimizing and evaluating models using loss functions and performance metrics, and creating repeatable and scalable training, evaluation, and test datasets**. The course is designed to help you understand the terminology used in machine learning and develop practical skills in applying ML techniques.



> TensorFlow - BigQuery - Machine Learning - Data Cleansing





  




## TensorFlow on Google Cloud

"TensorFlow on Google Cloud" is a course that teaches you how to design and build machine learning models using **TensorFlow 2.x and Keras**. You will learn about the key components of TensorFlow, manipulate data using the **tf.data** library, create models using the **Keras API**, and train and deploy models at **scale with Vertex AI**. By the end of the course, you will have the skills to build and improve machine learning models using TensorFlow on the Google Cloud platform.

>Tensorflow - Python Programming - Machine Learning - keras - Build Input Data Pipeline



### Introduction to the course

### Introduction to the TensorFlow Ecosystem

### Design and Build an Input Data Pipeline

### Building NN with TensorFlow and Keras API

### Training at scale with Vertex AI

### Summary



## Feature Engineering

This course, "Feature Engineering," is part of the Machine Learning on Google Cloud series. It focuses on improving the accuracy of machine learning models through feature engineering. The course covers various topics, including the use of **Vertex AI Feature Store**, moving from raw data to features, feature engineering using **BigQuery ML**, and Keras, preprocessing features using **Apache Beam and Cloud Dataflow**, and the use cases for **tf.Transform**. By the end of the course, learners will have a solid understanding of feature engineering techniques and how to apply them to enhance their machine learning models.



## Machine Learning Enterprise

This course, "Machine Learning in the Enterprise," is the fifth course in the Machine Learning on Google Cloud series. It takes a case study approach, **focusing on a team of data analysts, data scientists, software developers, and machine learning engineers** who are working on multiple ML and AI projects. The course covers various topics, including **data management, governance, and pre-processing options**, using Vertex AutoML, BigQuery ML, and custom training models, implementing Vertex Vizier Hyperparameter Tuning, creating batch and online predictions, setting up model monitoring, and creating pipelines using Vertex AI. The objective is to help learners understand how to apply machine learning in an enterprise setting. Let's get started!



## Production Machine Learning Systems

Welcome to the advanced machine learning on Google Cloud specialization. In this specialization, you will learn how to apply machine learning at **scale and build specialized machine learning models for images, sequences, and recommendations**. The first course focuses on building production machine learning systems and covers topics such as **static training, dynamic training, static inference, dynamic inference, and distributed training** using TensorFlow 2.0 with the Keras API. The second course is all about building image models, where you will learn about convolutional neural networks and build image classification models. The third course covers building sequence models, which are used for applications like financial time series prediction, speech recognition, and machine translation. Finally, the specialization ends with building real-world recommendation systems, bringing together all the concepts covered in the previous courses.



## Machine Learning Operations (MLOPs): Getting started

This course, "Machine Learning Operations (MLOps): Getting Started," is the first course in a series of machine learning operations topics. It is designed for machine learning data scientists, engineers, and analysts who are interested in learning about machine learning in the Cloud and using ML models and Vertex AI. The course covers the concept of MLOps, which aims to unify ML system development and operations. It also explores the challenges of operationalizing ML models and introduces the concept of **DevOps in machine learning**. The course further delves into the machine learning lifecycle and the importance of a unified platform like Vertex AI. Through hands-on labs, learners will work on a real-world use case and gain practical experience in training and deploying models with Vertex AI. By the end of the course, learners will have a comprehensive understanding of ML projects from an operational perspective and the significance of MLOps in the machine learning workflow.

 

## ML Pipelines on Google Cloud

"ML Pipelines on Google Cloud" is a comprehensive course that covers the management of machine learning pipelines on the Google Cloud platform. You will learn about **TensorFlow Extended (TFX)** and its role in managing ML pipelines and metadata. The course covers topics such as pipeline orchestration, custom components, continuous integration and deployment, ML metadata management, and automation of ML pipelines across multiple frameworks. You will also explore tools like **Kubeflow, AI Platform Pipelines, Cloud Composer, and MLflow**. By the end of the course, you will have a solid understanding of ML pipeline management and be able to improve your ML model development process on Google Cloud.