ABSTRACT
Upon completing this chapter, learners should be able to:
Describe commonly used software tools and libraries in machine learning development, including TensorFlow, PyTorch, scikit-learn, and Apache Spark.
Evaluate different hardware options for machine learning tasks based on performance, cost, and scalability considerations.
Demonstrate proficiency in setting up and configuring machine learning environments, including software installation, package management, and virtual environments.
Understand the importance of software version control and collaboration tools (e.g., Git and GitHub) in machine learning projects.
Explore cloud-based machine learning platforms and services for scalable model training and deployment.
