Author : Justin

Machine Learning

Machine Learning Concepts – Part 8 – Model Evaluation

Justin
by Justin
We briefly discussed model evaluation previously in our articles on problem definition and data collection, as well as model training. Today’s article will provide additional detail and instruction on model evaluation including how to choose the right metrics, how to...
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Machine Learning

Machine Learning Concepts – Part 7 – Hyperparameter Tuning

Justin
by Justin
In Machine Learning Concepts – Part 6 – Model Training we briefly glossed over hyperparameters in large language models. In this article we will continue to explore hyperparameters in large language models including what they are, how they are used,...
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Machine Learning

Machine Learning Concepts – Part 6 – Model Training

Justin
by Justin
In part 6 of our Machine Learning Concepts series we are going to discuss model training. Up until now we’ve identified the goal of our machine learning model, have pre-processed our data into a standard format, identified the features of...
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Machine Learning

Machine Learning Concepts – Part 5 – Model Selection

Justin
by Justin
So far in our ML Concepts series we’ve defined out problem and goal, we’ve identified, gathered and preprocessed the necessary data, and we’ve developed an understanding of our data through exploratory data analysis. Today’s article will introduce selecting the best...
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Machine Learning

Machine Learning Concepts – Part 4 – Exploratory Data Analysis

Justin
by Justin
Next up in our machine learning concepts series we are going to discuss exploratory data analysis (EDA). EDA is a cornerstone of data science; you can think of it as a process for you to get to know your data...
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