Google Machine Learning is a powerful tool developed by Google that uses artificial intelligence to create systems that can learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access and use data to learn for themselves. This tool is used in various sectors of technology and business to analyze data and predict future trends. Someone interested in building Google Machine Learning skills must learn about algorithms, data analysis, programming, and statistics. ‎

1. Machine Learning Engineer: These individuals design, implement, and deploy scalable machine learning solutions using platforms like TensorFlow. They might work on anything from refining search algorithms to designing new recommendation systems.

2. Data Scientist: While closely related to ML engineers, data scientists often focus more on data exploration, hypothesis testing, and deriving insights from vast amounts of data. They might use Google's ML tools to analyze data and develop predictive models.

3. Research Scientist (AI/ML): Especially prevalent at Google, these roles focus on advancing the frontier of machine learning. They'll develop new algorithms, refine existing ones, and work on practical and theoretical challenges in the field.

4. ML Solutions Architect: These professionals might work with business stakeholders to design ML-based solutions for specific business problems, ensuring that the technical solution aligns with business goals.

5. Technical Program Manager (Machine Learning): They oversee ML projects, ensuring resources, timelines, and stakeholders are aligned. They also work to bridge the gap between technical and non-technical teams.

6. AI Product Manager: These individuals guide the strategy and development of AI-driven products, ensuring that they meet user needs while being technically feasible.

7. Cloud Engineer specializing in ML services: With Google Cloud offering specialized services for machine learning (like AI Platform), there's demand for cloud engineers who specialize in setting up, deploying, and managing ML services on the cloud.

8. Application Developer with ML focus: Developers who incorporate ML features into apps, websites, or other software products. They might use Google's pre-trained models or APIs, like Vision API or Natural Language API, for specific functionalities. ‎

To get started with Google Machine Learning on Coursera:

ML Foundations: Enroll in introductory machine learning courses on Coursera, focusing on core concepts and techniques. Google's Frameworks: Dive into courses that cover Google's ML tools, especially TensorFlow and Google Cloud ML Engine. Hands-on Learning: Choose courses that provide practical exercises utilizing Google's machine learning platforms and technologies. Earn Certifications: Complete course materials and assessments to obtain certificates, showcasing your proficiency in Google's ML ecosystem. ‎