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11-05-2020 05:12 (edited 11-05-2020 05:16) by coursedrive
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Intro to Data Science Using Python: Your Best Starting Point

Learn About Data Science And Machine Learning Using Python To Start Your Career In Those Fields. The Best Starting Point


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What you'll learn

• Introduction to Data Science
• Data Science Most Used Packages
• Data Wrangling
• Model Development
• Model Refinement
• Model Evaluation Techniques

Requirements

• You must have a previous knowledge of Python
• Other than that, sit tight and watch carefully

Description

Welcome to “Introduction to Data Science Using Python” where you will set a good foot in the fields of Data Science and Machine Learning.
I'm your instructor Ali Desoki and I start from scratch going clearly over all the points in the course along with hands-on practical exercises and projects to summarize all the skills you’ve learned.
This course is designed for Beginners covering all Aspects of what you need to know to start in the fields of data science and machine learning with practice notebooks which summarize all the skills you’ve learned.
At the end of this course, you will be able to analyze and manipulate data with python and be able to start your career in this field.
This course covers a lot of useful and essential topics including:
Introduction to Data Science
Data Science Most Used Packages
Data Wrangling
Model Development
Model Refinement
Model Evaluation Techniques and more...
The ideal student for this course is someone who looks to start in the mentioned fields from scratch.
All you need to know is Python and basic statistics to start this course.
So what are you waiting for! Enroll now and jump-start your career in Data Science and Machine Learning.

Who this course is for:

• Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning
• Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career
• Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field

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