This module aims to develop knowledge from research activities to gain an understanding of international trade using Marketing , Social Media and how AI plays a role in International Marketing
Duration 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim
Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints
The “ISO 42001:2023 Lead Auditor Course” integrates the principles of ISO 42001:2023, the International Standard for Artificial Intelligence Management, with the methodologies outlined in ISO 19011:2018, the Guidelines for Auditing Management Systems. The course equips participants with the skills and knowledge required to lead Artificial Intelligence audits effectively, ensuring compliance with ISO 42001:2023, and applies the principles of ISO 17011:2017 for conformity assessment bodies.
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Becoming a Data Quality Expert Data science is an exploding field with tremendous demand. Having high quality data is an absolute must for any business today and data informs every decision a business must make. But what if you have poor quality data? What if your company acquired another company and the data structure does not match? What if you have large gaps in the data you have vs. what you need?Imagine yourself as an IT project/program manager who has run many engagements for the business. You have great PM skills and you run your agenda with the precision of a Swiss watch. But you now have to run Data Quality for your organization. Can you just program manage this and be fine? What will be different about this than any other IT project?Wake-up call: a WHOLE LOT! You must acquire a lot of new skills and you must become a data expert as quickly as possible. I want to share with you my journey and experience. I have had to go from deeply technical in some IT areas, to project/program managing general IT projects, to gaining specialized skills in data quality. I will share with you my assessment, gap analysis and mitigation strategy that transformed me into a data quality expert.
The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.
In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.