Description
The Practical Data Science Specialization is an applied science training program published by Coursera Academy. These two trainings are organized by the DeepLearning.AI and mazon Web Services foundations. During the training process, you will become familiar 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 production and implementation environment of the final product and require fewer prerequisites and considerations to develop decades. Moving data-driven, machine-based projects from the conceptualization and design phase to production of the final product requires dispersed skill sets that not all developers possess. The overall architecture and structure of your project should be such that you provide the best performance with the least amount of resources and the process of development and use is simple.
Science is a broad, interdisciplinary industry that requires skills in math, statistics, statistics, and programming. This training program is entirely dedicated to developers, analysts and knowledge designed to deal with data on a daily basis and its candidates must be designed in the programming language of Python, SQL and systems. Master database management.
What you will learn in the Practical Data Science Specialization
- Initial data collection and preparation
- Detection of biases and flaws in raw statistical data
- Train, 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
- machine learning
- Multiple classification with FastText and BlazingText libraries
- Prohibited data
- Exploratory data analysis
- Evaluate and troubleshoot different machine learning models
Course specifications
Editor: Coursera
Instructors: Antje BarthShelbee Eigenbrode, Sireesha Muppala and Chris Fregly
French language
Advanced level
institution/university: DeepLearning.AI and Amazon Web Services
Number of Courses: 3
Duration: Approximately 3 months to complete – Suggested pace of 5 hours/week
Course 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 specialization prerequisites in data science
What basic knowledge is needed for the practical specialization in data science?
Learners should have a working knowledge of ML algorithms and principles, master Python programming at an intermediate level, and be familiar with Jupyter notebooks and statistics. We recommend that you take 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
Sample Data Science Practical Specialization Video
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
This specialization contains 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
File password(s): ngaur.com
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In total, about 991 MB