Gain a thorough understanding of the ideas behind big data analysis and machine learning on the Google Cloud Platform.
Big data is the enormous amount of data that organizations have access to as a result of its scale and complexity, which makes it difficult for many analytics tools to handle or evaluate. The amount of data acquired, the frequency at which it is made available to an organization for evaluation, and the complexity or variety of that data can all be aided by tools for big data. The Google Cloud Big Data and Machine Learning Fundamentals online certification was created by Google Cloud and is taught by Google Cloud Training, which is provided through Coursera.
Google Cloud Big Data and Machine Learning Fundamentals online classes introduce the learners to the methods associated with big data and machine learning services on Google Cloud and products that enable the data-to-AI lifecycles. The Google Cloud Big Data and Machine Learning Fundamentals online course covers the steps, difficulties, and advantages of establishing a big data infrastructure and machine learning models using functionalities of Vertex AI on Google Cloud as well as the characteristics of tools like Qwiklabs, Apache Beam, AutoML, TensorFlow, BigQuery, and others.
Google Cloud Big Data and Machine Learning Fundamentals Fee Details -
Description
Total Fee in INR
Course Fee, 1 month
Rs. 4,117
Course Fee, 3 months
Rs. 8,234
Course Fee, 6 months
Rs. 12,352
Yes
Coursera
After completing the Google Cloud Big Data and Machine Learning Fundamentals certification course, learners will gain a solid understanding of the core principles and methodologies associated with google cloud and big data for operations like cloud computing, machine learning, big data analytics, and data visualization. Learners will explore functionalities of Qwiklabs, Vertex AI, BigQuery, TensorFlow, Locker, data studio, and AutoML for activities like big data analysis and data visualization. Learners will study strategies associated with data operations like data engineering, data preparation, and data streaming. Learners will also study techniques for model training, model evaluation, model deployment, and model monitoring.
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