Description
Practical Data Science Specialization is an applied data science training package published by Corsara Academy. This training series is organized by DeepLearning.AI and Amazon Web Services, and during the training process, you will get acquainted with the process of developing, scaling, and implementing data science projects on Amazon SageMaker cloud servers.
The development environment is very different from the final product production and implementation environment and requires fewer prerequisites and considerations from developers. Transferring data science and machine learning projects from the idea generation to the final product requires a scattered set of skills that not every developer has. The overall architecture and structure of your project should be such that it offers the best performance with the least resources and the process of process development and scalability is easy.
Data science is a vast interdisciplinary industry that requires a variety of skills in mathematics, statistics, data illustration, and programming. This training suite is designed exclusively for developers, analysts, and scientists who deal with data on a daily basis, and students and applicants are expected to speak Python, SQL, and a number of systems. Master database management.
What you will learn in the Practical Data Science Specialization training suite:
- Initial data collection and preparation
- Detection of biases and defects of raw statistical data
- Practice, evaluate, and optimize different models using AutoML
- Design, implement, monitor, and manage machine learning pipeline operations
- Natural language processing with the BERT library
- A / B testing of different machine learning models
- Automatic machine learning
- Multiple classifications with FastText and BlazingText libraries
- Forbidden data
- Exploratory data analysis
- Evaluate and troubleshoot different machine learning models
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Practical Data Science Specialization Course specifications
Publisher: Coursera
Instructor: Antje Barth , Shelbee Eigenbrode , Sireesha Muppala , and Chris Fregly
Language: English
Institute / University Provider: DeepLearning.AI and Amazon Web Services
Level of Training: Advanced
Number of Courses: 3
Duration of Training: Assuming 5 hours of work per week for about 3 months
Courses in the Practical Data Science Specialization series
Course 1
Analyze Datasets and Train ML Models using AutoML
Course 2
Build, Train, and Deploy ML Pipelines using BERT
Course 3
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
Course Prerequisites
What background knowledge is necessary for the Practical Data Science Specialization?
Learners should have a working knowledge of ML algorithms and principles, be proficient in Python programming at an intermediate level, and be familiar with Jupyter notebooks and statistics. We recommend you complete the Deep Learning Specialization or an equivalent program.
Learners should also be familiar with the fundamentals of AWS and cloud computing. Completion of Coursera AWS Cloud Technical Essentials or similar is considered the prerequisite knowledge base.
Practical Data Science Specialization Course pictures
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This collection includes 3 different courses.
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Analyze Datasets and Train ML Models using AutoML
Build, Train, and Deploy ML Pipelines using BERT
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
Download course – 304 MB
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