Advanced Machine Learning on Google Cloud Specialization 

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp). One of the best ways to review something is to work with the concepts and technologies that you have learned.

So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform

Prerequisites:

Basic SQL, familiarity with Python and TensorFlow

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

This is the third course of the Advanced Machine Learning on GCP specialization. In this course,

We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don?t have enough data and how to incorporate the latest research findings into our models.

You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we?ll work on together.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.

? Predict future values of a time-series

? Classify free form text

? Address time-series and text problems with recurrent neural networks

? Choose between RNNs/LSTMs and simpler models

? Train and reuse word embeddings in text problems

You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we?ll work on together.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

? Devise a content-based recommendation engine

? Implement a collaborative filtering recommendation engine

? Build a hybrid recommendation engine with user and content embeddings