Machine Learning for Business Professionals from Google
Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.
Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month.That means,there is always exponential need for Machine learning engineers to build, implement, and maintain machine learning systems,algorithms in technology products with focus on machine learning system reliability, performance, and scalability.
In this post,we check out new course from Google,this course is intended to be an introduction to machine learning for non-technical business professionals.
Machine Learning for Business Professionals
By the end of this course, you will have learned how to:
- Formulate machine learning solutions to real-world problems
- Identify whether the data you have is sufficient for ML
- Carry a project through various ML phases including training, evaluation, and deployment
- Perform AI responsibly and avoid reinforcing existing bias
- Discover ML use cases
- Be successful at ML
You’ll need a desktop web browser to run this course’s interactive labs via Qwiklabs and Google Cloud Platform.
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What you will learn from this course
#2.What is Machine Learning?
#4.Discovering ML Use Cases
#5.How to be successful at ML
Additional Resources :
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- Applied AI: Artificial Intelligence with IBM Watson Specialization
- Python Curated Course Collection
- AI Foundations for Everyone Specialization from IBM
- ULTIMATE GUIDE to Coursera Specializations That Will Make Your Career Better (Over 100+ Specializations covered)
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[…] the time-series data available and stored in our Digital Twin Server, our next step is to build an ML model based on the data collected to predict failure based on core attributes such as CPU, Memory, […]