MMLPGCGOGoogleGO-MMLPGC1.0<ul> <li>Thoroughly understand how ML can be used to improve business processes and create new value.</li><li>Explore common machine learning use cases implemented by businesses.</li><li>Identify the requirements to carry out an ML project, from assessing feasibility, to data preparation, model training, evaluation, and deployment.</li><li>Define data characteristics and biases that affect the quality of ML models.</li><li>Recognize key considerations for managing ML projects, including data strategy, governance, and project teams.</li><li>Pitch a custom ML use case that can meaningfully impact your business.</li></ul><ul> <li>No prior technical knowledge is required.</li><li>Savvy about your own business and objectives.</li><li>Recommended: Business Transformation with Google Cloud (on-demand).</li></ul><ul> <li>Enterprise, corporate, or SMB business professionals in non-technical roles. Roles include but are not limited to: business analysts, IT managers, project managers, and product managers.</li><li>For senior VPs and above, Data-Driven Transformation with Google Cloud (ILT) is more suitable.</li></ul><h4>Module 01: Introduction</h4><ul> <li>Differentiate between AI, machine learning, and deep learning.</li><li>Describe the high-level uses of ML to improve business processes or to create new value.</li><li>Begin assessing the feasibility of ML use cases.</li></ul><h4>Module 02: What is Machine Learning</h4><ul> <li>Differentiate between supervised and unsupervised machine learning problem types.</li><li>Identify examples of regression, classification, and clustering problem statements.</li><li>Recognize the core components of Google&rsquo;s standard definition for ML and considerations for each when carrying out an ML project.</li></ul><h4>Module 03: Employing ML</h4><ul> <li>Describe the end-to-end process to carry out an ML project and considerations within each phase.</li><li>Practice pitching a custom ML problem statement that has the potential to meaningfully impact your business.</li></ul><h4>Module 04: Discovering ML Use Cases</h4><ul> <li>Discover common machine learning opportunities in day-to-day business processes</li></ul><h4>Module 05: How to be Successful at ML</h4><ul> <li>Identify the requirement for businesses to successfully use ML</li></ul><h4>Module 06: Summary</h4><ul> <li>Summarize key concepts and tools covered in the course content.</li><li>Compete for best ML use case presentation based on creativity, originality, and feasibility.</li></ul>- Thoroughly understand how ML can be used to improve business processes and create new value. - Explore common machine learning use cases implemented by businesses. - Identify the requirements to carry out an ML project, from assessing feasibility, to data preparation, model training, evaluation, and deployment. - Define data characteristics and biases that affect the quality of ML models. - Recognize key considerations for managing ML projects, including data strategy, governance, and project teams. - Pitch a custom ML use case that can meaningfully impact your business.- No prior technical knowledge is required. - Savvy about your own business and objectives. - Recommended: Business Transformation with Google Cloud (on-demand).- Enterprise, corporate, or SMB business professionals in non-technical roles. Roles include but are not limited to: business analysts, IT managers, project managers, and product managers. - For senior VPs and above, Data-Driven Transformation with Google Cloud (ILT) is more suitable.Module 01: Introduction - Differentiate between AI, machine learning, and deep learning. - Describe the high-level uses of ML to improve business processes or to create new value. - Begin assessing the feasibility of ML use cases. Module 02: What is Machine Learning - Differentiate between supervised and unsupervised machine learning problem types. - Identify examples of regression, classification, and clustering problem statements. - Recognize the core components of Google’s standard definition for ML and considerations for each when carrying out an ML project. Module 03: Employing ML - Describe the end-to-end process to carry out an ML project and considerations within each phase. - Practice pitching a custom ML problem statement that has the potential to meaningfully impact your business. Module 04: Discovering ML Use Cases - Discover common machine learning opportunities in day-to-day business processes Module 05: How to be Successful at ML - Identify the requirement for businesses to successfully use ML Module 06: Summary - Summarize key concepts and tools covered in the course content. - Compete for best ML use case presentation based on creativity, originality, and feasibility.2 days1495.001300.001700.001300.001300.001300.004510.001495.001495.001325.001325.001325.001300.001320.002065.001550.00