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Python IDEs for Machine Learning

Learn Python IDEs for Machine Learning from basics in this free online training. The course covers popular IDEs such as Jupyter Notebook, Spyder & includes practical exercises to help learners gain hands-on experience.

Instructor:

Mr. Anirudh Rao
4.5
average rating

Ratings

Beginner

Level

3.75 Hrs

Learning hours

1.4K+

Learners

Skills you will learn

Introduction to Python IDEs
Spyder
Google Colab
Jupyter Notebook

About this Course

Machine learning is an essential part of the IT landscape today with applications in various fields like education, medicine, finance, and more. Python is one of the forerunners when it comes to machine learning development. So it is important for us to know about the various IDEs that can be used for Programming in Python for Machine learning. 

 


The free Python IDEs for Machine Learning course covers the basics of Jupyter Notebook and its key features and insights, along with the installation and interface walkthrough. Learners will also get an introduction to Google Colab, a cloud-based Jupyter notebook, and its features. Additionally, the course includes a walkthrough of other popular Python IDEs such as Spyder and R. With practical exercises, learners can gain hands-on experience in using these IDEs for machine learning tasks. This course is suitable for beginners and professionals looking to enhance their skills in Python IDEs for machine learning.
 

Why upskill with us?

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1000+ free courses
In-demand skills & tools
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Free life time Access

Course Outline

Introduction to Jupyter notebook in Python
Key features and insights about Jupyter Notebook in Python
Installing Jupyter Notebook and Interface walkthrough
Introduction to Google Colab
Walkthrough of various Python IDEs - Spyder and R

Python IDEs for Machine Learning

4.5
average rating

Ratings

3.75 Hrs

Learning hours

Beginner

Level

1.4K+

Learners

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Our course instructor

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Mr. Anirudh Rao

Machine Learning Expert

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747.2K+ Learners
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81 Courses
Anirudh has been working in the field of Data Science and has expertise over Python, Machine Learning and other concepts in the field of data analysis. He is also proficient in the concept of Deep Learning and its usage in a production environment. Expertise extends towards working on various projects in the domain of Artificial Intelligence and Neural Networks as well.

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