Description
Practical Data Science Specialization is a practical data science training series published by Coursera Academy. This educational series was organized by DeepLearning.AI and mazon Web Services foundations, and during its educational process, you will get to know the process of developing, scaling and implementing data science projects on the platform of Amazon SageMaker cloud servers. The development environment is very different from the production environment and the final implementation of the product and requires fewer prerequisites and considerations from the developers. Transferring projects based on data science and machine learning from the ideation stage and initial design to the production of the final product requires a set of scattered skills that not every developer has these skills. The architecture and overall structure of your project should be such that it provides the best performance with the least resources, and the process of development and scalability should be easy.
Data science is an interdisciplinary and very broad industry that requires various skills in the fields of mathematics, statistics, data visualization and programming. This training series is designed exclusively for developers, analysts and scientists who deal with data on a daily basis, and its students and applicants are expected to learn Python, SQL and a number of systems. Master database management.
What you will learn in the Practical Data Science Specialization training series:
- Data collection and initial preparation
- Diagnosing biases and flaws in raw statistical data
- Practice, evaluate and optimize different models using AutoML
- Design, implement, monitor and manage machine learning pipeline operations
- Natural language processing with libraries BERT
- A/B testing of different machine learning models
- Automatic machine learning
- Multi-class classification with libraries FastText and BlazingText
- data mining
- Exploratory data analysis
- Evaluation and troubleshooting of different machine learning models
Course details
Publisher: Coursera
teacher: Antje Barth ,Shelbee Eigenbrode ,Sireesha Muppala And Chris Fregly
English language
Provider institution/university: DeepLearning.AI and Amazon Web Services
Training level: advanced
Number of courses: 3
Duration of training: assuming 5 hours of work per week, about 3 months
Courses available in 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.
Course images
Sample video of the Practical Data Science Specialization course
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
This collection includes 3 different courses.
download link
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
Password file(s): www.downloadly.ir
Size
Total about 991 MB
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