Serverless Machine Learning with Tensorflow on Google Cloud em Português Brasileiro

Google Cloud via Coursera

Go to Course: https://www.coursera.org/learn/serverless-machine-learning-gcp-br

Introduction

### Course Review: Serverless Machine Learning with Tensorflow on Google Cloud (em Português Brasileiro) #### Overview In today’s tech-driven world, understanding and utilizing machine learning (ML) has become increasingly essential. For Portuguese-speaking individuals and professionals keen on diving into the realm of ML, Coursera offers an invaluable course titled **"Serverless Machine Learning with Tensorflow on Google Cloud em Português Brasileiro."** This four-day intensive, on-demand course serves as an entry point into designing and developing machine learning systems using the Google Cloud Platform (GCP). Through a well-structured blend of presentations, demonstrations, and hands-on labs, participants are guided to understand the fundamental concepts of machine learning and the powerful TensorFlow framework while acquiring practical skills in the development, evaluation, and production of ML models. #### Course Objectives This course is designed to equip participants with a solid foundation in machine learning and TensorFlow, focusing on the following key skills: 1. **Identifying the Basics**: A clear understanding of machine learning concepts. 2. **Creating ML Models**: Utilizing TensorFlow to build robust machine learning models. 3. **Scaling ML Models**: Implementing the Cloud ML Engine to scale models effectively. 4. **Feature Engineering**: Learning the intricacies of attribute engineering for optimizing model performance. #### Syllabus Breakdown The course is structured into four comprehensive modules, each targeting essential aspects of serverless machine learning: - **Módulo 1: Primeiros passos com machine learning** - This foundational module introduces participants to the principles of machine learning. It highlights real-world applications and explores the fundamental concepts that underpin ML technologies. - **Módulo 2: Criação de modelos de ML com o TensorFlow** - Here, participants get hands-on experience with TensorFlow, learning how to create, train, and validate machine learning models. The practical exercises sharpen skills and enhance understanding of the TensorFlow ecosystem. - **Módulo 3: Escalonamento de modelos de ML com o Cloud ML Engine** - This module focuses on deploying and scaling ML models using the Google Cloud ML Engine, empowering participants to manage and optimize their machine learning solutions in a cloud environment effectively. - **Módulo 4: Engenharia de atributos** - In the final module, participants delve into feature engineering, learning how to refine data inputs to maximize the predictive performance of their models. This is crucial for anyone serious about developing high-quality ML applications. #### Recommendation The **"Serverless Machine Learning with Tensorflow on Google Cloud"** course is a fantastic opportunity for Brazilian Portuguese speakers to enhance their data science and machine learning skills. Whether you are a beginner looking to get started or an experienced developer aiming to deepen your understanding of serverless architecture and cloud applications, this course provides valuable insights and practical knowledge. The hands-on labs in particular are a highlight, as they enable participants to translate theory into practice, ensuring that the learning is both engaging and applicable. Furthermore, the course is taught in Portuguese, making it accessible to a broader audience without the barrier of language. Ultimately, I highly recommend this course for those looking to leverage machine learning innovations and implement them in practical, real-world scenarios using Google Cloud. It promises to be a worthwhile investment in your professional development in the rapidly evolving landscape of artificial intelligence and machine learning.

Syllabus

Este é o "Serverless Machine Learning on Google Cloud Platform"

Módulo 1: Primeiros passos com machine learning

Módulo 2: Criação de modelos de ML com o TensorFlow

Módulo 3: Escalonamento de modelos de ML com o Cloud ML Engine

Módulo 4: Engenharia de atributos