Google Certification Course Overview

Machine learning courses often cover a variety of topics, including:

Here are some of the skills you'll learn in the course:

• Introduction to machine learning: Covers the fundamentals of machine learning, including popular algorithms and concepts
• Programming: Students learn to program in Python or R, and use libraries like NumPy, Pandas, and Matplotlib
• Data structures and algorithms: Students learn the foundations for developing models
• Supervised learning: Students learn about regression and classification algorithms, such as linear regression, decision trees, and SVM
• Unsupervised learning: Students learn about clustering and dimensionality reduction
• Deep learning: Students learn about neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
• Natural language processing (NLP): Students learn about text analytics and sentiment analysis
• Model evaluation and tuning: Students learn about cross-validation, overfitting, and model optimization
• Big data and cloud integration: Students learn how to work with large datasets and use cloud services like AWS and Azure
• Capstone project: Students apply their learned concepts to a real-world problem

Here are some machine learning courses:

• Introduction to Machine Learning - IITKGP - Swayam - NPTEL:This course covers the fundamentals of machine learning, popular algorithms, and computational learning theory. It also includes hands-on problem solving with Python.
• Machine Learning Course - Intel: This course is structured around 12 weeks of lectures and exercises, and encourages familiarity with Python.
• Machine Learning Crash Course - Google for Developers: This course covers the fundamentals of working with categorical data, including how to represent it numerically.
• Understanding Machine Learning Course - DataCamp : This course provides a non-technical introduction to machine learning concepts, including the machine learning workflow and different types of models.
• Unsupervised learning: Students learn about clustering and dimensionality reduction
• Deep learning: Students learn about neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
• Natural language processing (NLP): Students learn about text analytics and sentiment analysis
• Model evaluation and tuning: Students learn about cross-validation, overfitting, and model optimization
• Big data and cloud integration: Students learn how to work with large datasets and use cloud services like AWS and Azure
• Capstone project: Students apply their learned concepts to a real-world problem

To become a Google Cloud Certified Professional Machine Learning Engineer, you can:

• Enroll and pass the GCP Professional Machine Learning Engineer certification exam
• Score at least 70% on the exam
• Pay $200 plus tax (where applicable)