ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning

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In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming:

ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning.

What should I learn from this Applied Machine Learning & Data Science tutorials?

You will learn:

  • ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning.
  • Practical Data Science tutorials with R for Beginners and Citizen Data Scientists.
  • Practical Machine Learning tutorials with R for Beginners and Machine Learning Developers.

 

For Citizen Data Scientists and Machine Learning Developers: Download 1000+ End-to-End Applied Machine Learning & Data Science Notebooks in Python and R for Beginners to Professionals.

 

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ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning:



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Call for Jupyter Notebook Challenge: Business Data Science & Machine Learning (Classification, Regression and Forecasting) @ https://wacamlds.podia.com

We are very pleased to let you know that WACAMLDS (https://wacamlds.podia.com) is hosting Jupyter Notebook Challenges for Business Data Science & Machine Learning. The author(s) of the best notebook will receive a prize valued $150 USD.

For details criteria and eligibility, please see below:

Theme: Jupyter Notebook Challenge for Business Data Science & Machine Learning (Classification, Regression and Forecasting). You can choose any dataset to present your notebook.

Eligibility: Only WACAMLDS members can participate (either FREE member or Gold member).

Submission Format:

  • a) Your notebook must be submitted as .html file as export from Jupyter notebook or Jupyter lab. Choose any language – Python or R or Julia (with or without SQL).
  • b) No need to submit any dataset(s) along with the submission file (.html).
  • c) First cell of the notebook must have a title, author(s) name, affiliation and email address of corresponding author.
  • d) Second cell of the notebook must have a very brief description of the dataset used.
  • e) Remaining cells (input and output) should have your end-to-end analysis, model development, results etc.

How to submit: visit WACAMLDS website for details.

Prize & Benefit:

  • a) $150 USD
  • b) Certificate of Achievement
  • c) Share the winner details and the best notebook to the data scientist professional groups.
  • d) Top ranked notebooks will be available through WACAMLDS.

Download End-to-End Notebooks in Python and R for Citizen Data Scientists and Machine Learning Developers from https://wacamlds.podia.com/end-to-end-notebooks-for-citizen-data-scientists?coupon=WACAMLDS80

 

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

 

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For Citizen Data Scientists and Machine Learning Developers: Download 1000+ End-to-End Applied Machine Learning & Data Science Notebooks in Python and R for Beginners to Professionals.