Google Cloud AI Platform Learning Path

This course introduces the AI and machine learning offerings on Google Cloud for both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, covering AI foundations, development, and solutions. The course is aimed at data scientists, AI developers, and ML engineers, offering engaging learning experiences and practical hands-on exercises.

This specialization from Google Cloud on Coursera teaches how to build and deploy ML models on Google Cloud Platform. It covers Vertex AI, AutoML, Big Query ML, and TensorFlow, preparing learners for a career in cloud-based machine learning.

This course teaches how to apply knowledge of classification models and embeddings to build a machine learning pipeline that functions as a recommendation engine using TensorFlow on Google Cloud Platform.

This course on Pluralsight teaches how to train custom machine learning models on your own datasets using Google Cloud AutoML. It covers the underlying concepts of neural architecture search and transfer learning used by AutoML.

Google AI for Everyone Professional Certificate

Learn about machine learning, AI, and deep learning with Google Cloud. Covers TensorFlow, AutoML, and cloud AI services.

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. TensorFlow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate deep learning. TensorFlow is one of the most comprehensive libraries for implementing deep learning.This comprehensive 2-in-1 course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as as TensorFlow, Keras, and Google Cloud MLE. This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow, is designed to help you overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. You will then learn deep neural networks for different problems and explore the applications of convolutional neural networks on two real datasets. You will also learn how autoencoders can be used for efficient data representation. Finally, you will understand some of the important techniques to implement generative adversarial networks.The second course,

Machine Learning, Big Query, Tensor Board, Google Cloud, TensorFlow, Deep Learning have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help engineers, MBA students and young managers learn the fundamentals of big data and data science and their applications in business scenarios. In this course you will learn1. Data Science2. Machine Learning3. Big Query4. Tensor Board5. Google Cloud Machine Learning6. AI, Machine Learning, Deep Learning Fundamentals7. Analyzing Data8. Supervised and Unsupervised Learning9. Building a Machine Learning Model Using Big Query 10. Building a Machine Learning Model Using GCP and Tensorboard11. Building your own model for predicting diabetes using Decision Tree