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How to Convert Jupyter Notebook into ML Web App?

Analytics Vidhya

Introduction Jupyter Notebook is a web-based interactive computing platform that many data scientists use for data wrangling, data visualization, and prototyping of their Machine Learning models. It is easy to use the platform, and we can do programming in many languages like Python, Julia, R, etc. […].

ML 367
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How To Learn Python For Data Science?

Pickl AI

Summary: Python for Data Science is crucial for efficiently analysing large datasets. With numerous resources available, mastering Python opens up exciting career opportunities. Introduction Python for Data Science has emerged as a pivotal tool in the data-driven world. in 2022, according to the PYPL Index.

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Data Wrangling with Python

Mlearning.ai

Because it can swiftly and effectively handle data structures, carry out calculations, and apply algorithms, Python is the perfect language for handling data. Data wrangling requires that you first clean the data. It entails searching the data for missing values and assigning or imputed values to them.

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Journeying into the realms of ML engineers and data scientists

Dataconomy

Key skills and qualifications for machine learning engineers include: Strong programming skills: Proficiency in programming languages such as Python, R, or Java is essential for implementing machine learning algorithms and building data pipelines.

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Supercharge your skill set with 9 free machine learning courses

Data Science Dojo

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. Machine Learning with Python by Andrew Ng This is an intermediate-level course that teaches you more advanced machine-learning concepts using Python.

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Speed up Your ML Projects With Spark

Towards AI

As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for data wrangling.

ML 80
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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation. What percentage of machine learning models developed in your organization get deployed to a production environment?