This course provides participants with practical training in building, training, and deploying machine learning models using Google Cloud AI Platform and TensorFlow. Attendees will explore the powerful tools and services offered by Google Cloud for data preparation, model training, and deployment. By the end of the course, participants will be equipped to leverage Google Cloud’s infrastructure for efficient and scalable machine learning workflows.
Day 1
Session 1: Introduction to Google Cloud AI Platform
Session 2: Data Preparation with Google Cloud
Session 3: Building Machine Learning Models with TensorFlow
Day 2
Session 1: Advanced Machine Learning with TensorFlow
Session 2: Using AutoML for Simplified ML Model Creation
Session 3: Deploying ML Models on Google Cloud AI Platform
Day 3
Session 1: Scaling Machine Learning Workloads with Google Cloud
Session 2: Monitoring and Optimizing ML Models
Session 3: Case Study: Real-World Machine Learning with Google Cloud
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.
This introductory course provides participants with a foundational understanding of machine learning (ML) and artificial intelligence (AI).
This hands-on course introduces participants to machine learning using Python, focusing on foundational concepts and practical implementation.
This course introduces participants to machine learning using TensorFlow, a powerful open-source framework for building ML models and neural networks.
This course provides practical training in preparing data for machine learning models, focusing on data preprocessing and feature engineering techniques.
This course provides an in-depth understanding of supervised and unsupervised learning techniques, covering essential concepts such as classification, regression, clustering, and dimensionality reduction.
This course introduces participants to the fundamentals of machine learning and its implementation using R.
This course provides hands-on training in developing and deploying machine learning models using Microsoft Azure ML Studio’s intuitive drag-and-drop interface.
This hands-on course introduces participants to Amazon SageMaker, a powerful cloud-based machine learning service.
This course provides a beginner-friendly introduction to neural networks and deep learning concepts using Keras, a high-level API of TensorFlow.
This course bridges the gap between machine learning and business strategy by focusing on practical applications of ML techniques to solve business challenges.
This course provides an introduction to machine learning techniques and tools in MATLAB, focusing on practical applications for data analysis and modeling.
This course introduces participants to the concepts of Explainable AI (XAI) and ethical considerations in machine learning.
This course introduces participants to the field of computer vision, focusing on image processing and object detection using Python and the OpenCV library.
This course provides an introduction to machine learning capabilities in Excel and Power BI, focusing on creating predictive models and generating actionable insights.