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So far we covered the fundamental concepts of ml, setup on ubuntu and initial model creation in python using scikit-learn library. Most of the text so far is about basics of ML. And next steps- 1. Dive into the actual application idea 2. Understand the available dataset 3. Start to cover the code. Network infrastructure…
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In last post we created a setup for Machine learning project and in this part we move on with writing code- importing model from library and configuring hyperparameters for the same Configuring a Scikit-learn Model for Training In the script, we are creating multiple models- random_forest, xgboost & lightgbm, each one of these can address…
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Believe it or not, the latest chipset model from NVIDIA is not essential to start doing projects on machine learning. one can run the scripts that train the ML model on a 16GB RAM laptop running Ubuntu LTS without any additional hardware. (no harm if you do have ml processors- it makes the training quicker…
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Even though I think I understand the general concepts of machine learning(I also believe in dreamcatchers and monarchy), I often struggle to put theory into practice. These posts document my journey of learning ML through hands-on implementation. As a complete newbie to machine learning practice, I will start with an idea and then, step by…