Let's see how this works: Load data in Pandas and define a "ProblemDefinition" via dictionary specifying the name of the target column. Now Create a JSON-AI syntax using "json_ai_from_problem" function From Json AI generate a python code using "code_from_json_ai"
@sumanth_077
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MindsDB Launches Lightwood AutoML Framework with JSON-AI Syntax
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Introducing "Lightwood" from MindsDB which is an AutoML framework This helps you to generate and customize machine learning pipelines using a syntax called JSON-AI.
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AutoML Framework Automates Machine Learning Pipeline Generation
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In machine learning, we spend a lot of time writing the same set of code for cleaning, data preparation and model building Here's an AutoML framework that takes data, target variable as input and generates an entire machine-learning pipeline for you Thread
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Kaggle Solutions Repository with Top Performers’ Best Ideas
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5. Kaggle Solutions This repository give access to almost all available best solutions and ideas shared by top performers in the Kaggle competitions:
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Python ML MLOps CV NLP LLMs Daily Tutorials
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If you are interested in: – Python – Machine Learning – MLOps – CV/NLP – LLMs Find me → @Sumanth_077 Everyday, I share tutorials on above topics! Like/RT the first tweet to help this reach more people!
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Data Science IPython: Jupyter Notebook Machine Learning Libraries Guide
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4. Data Science IPython There are many Libraries in Machine Learning so having a Jupyter Notebook Explanations to all those is really handy. This Repository will help you with that.
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Best MLOps Repository for Learning Machine Learning Foundations
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3. Made with ML Best Repository to Learn MLOps. Also help you learn the foundations of machine learning through intuitive explanations, clean code and visualizations.
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Deep Learning Drizzle: Free Courses from Top Universities
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2. Deep Learning Drizzle This is an organized website where you can find all the FREE and best courses in: → Machine Learning
→ NLP
→ Computer Vision
→ Reinforcement Learning from Top Universities (all in one place) -
Awesome Machine Learning: Curated Frameworks, Libraries and Software
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1. Awesome Machine Learning A repository with a curated list of awesome machine learning frameworks, libraries and software:
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Analyzing Model Results Through Summary and Quality Tabs
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You can even analyze and evaluate the model results. In the Summary tab, you can find info about the input features of the model. In the Quality tab, you can see evaluation metrics about the model and similar info about the Dataset and variable importance. 5/6