Coursera – Practical Data Science Specialization 2022-5
Coursera – Practical Data Science Specialization 2022-5 Downloadly IRSpace
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Practical Data Science Specialization is an applied science training package published by the Coursera Academy. These two trainings are organized by DeepLearning.AI and mazon Web Services foundations, and during the training process, you will get acquainted with the process of developing, scaling, and implementing data science projects on the Amazon SageMaker cloud server platform. Development environments are very different from the final product production and implementation environment, and require fewer prerequisites and considerations to develop decades. The transfer of data-driven and machine-based projects from the conceptualization and design stage to the production of the final product requires scattered sets of skills that not every developer possesses. The overall architecture and structure of your project should be such that you provide the best performance with the least resources and the process of development and use is easy.
Science is a vast interdisciplinary industry that requires skills in mathematics, statistics, statistics, and programming. This training package is completely dedicated to developers, analysts and knowledge that is designed to deal with data on a daily basis and its applicants are expected to be designed in the programming language of Python, SQL and systems . Master database management.
What you will learn in Practical Data Science Specialization
- 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 classification with FastText and BlazingText libraries
- Forbidden data
- Exploratory data analysis
- Evaluate and troubleshoot different machine learning models
Course Specifications
Publisher: Coursera
Instructors: Antje Barth, Shelbee Eigenbrode, Sireesha Muppala and Chris Fregly
Language: English
Level: Advanced
institution/university: DeepLearning.AI and Amazon Web Services
Number of Courses: 3
Duration: Approximately 3 months to complete – Suggested pace of 5 hours/week
Courses included:
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
Practical Data Science Specialization 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.
Pictures
Practical Data Science Specialization Sample Video
Installation Guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
This Specialization contain 3 courses.
Download Links
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
Size
In total, about 991 MB
