Skytowner Guides on Machine Learning
“Skytowner Guides on Machine Learning” provides a comprehensive overview of machine learning concepts and applications. The content delves into building machine learning models, running experiments, and interpreting results. It emphasizes the importance of understanding data augmentation, deterministic processes, and fault-tolerant architectures in machine learning projects. Additionally, the guide explores the challenges of working with big data, the significance of context in AI applications, and the use of generative AI for enterprise solutions. Overall, the guide aims to equip readers with practical insights and strategies for successful machine learning implementation.
Comprehensive Guide on Cross Validation
Cross validation is a technique to measure the performance of a model through resampling. It is a standard practice in machine learning to split the dataset into training and testing sets. The trainin...
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Comprehensive Guide on Mean Absolute Error (MAE)
Mean absolute error , or MAE, measures the performance of a regression model. The mean absolute error is defined as the average of the all absolute differences between true and predicted values: $$\ma...
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Comprehensive Guide on ROC Curve
The ROC (Receiver Operating Characteristic) curve is a way to visualise the performance of a binary classifier. Confusion matrix Consider the following confusion matrix , which is essentially just a s...
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Comprehensive Guide on Logistic Regression
Logistic regression is a popular classifier that predicts the probability of a binary event occurring. For instance, suppose we were given past data about the number of hours students spent studying a...
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Gentle but Comprehensive Guide to Naive Bayes
Simple dataset Consider the following dataset: Color Size Target Brown Small Cat Black Large Dog White Large Dog Black Large Cat Brown Small Cat Here, color is a categorical variable with 3 levels (Br...
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Simple linear regression
Linear regression is one of the most popular and simplest machine learning models that capture the relationship between two or more features. The objective of linear regression is to draw a line of be...
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Comprehensive Guide to k-means Clustering
k-means is perhaps the most popular unsupervised algorithm (requires no labeled data) for clustering data points. The objective of k-means is to partition the data into a predefined number of clusters...
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Comprehensive Guide on DBSCAN
DBSCAN , or Density-Based Spatial Clustering of Applications with Noise , is a clustering technique that relies on density to group data points. The basic idea behind DBSCAN is that points that are cl...
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Getting Started with PySpark
PySpark is an API interface that allows you to write Python code to interact with Apache Spark, which is an open source distributed computing framework to handle big data. As the size of data grows ye...
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Applying a custom function on PySpark Columns with user-defined functions
PySpark comes with a rich set of built-in functions that you can leverage to implement most tasks, but there may be cases when you would have to roll out your own custom function. In PySpark, we can e...
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Getting Started with PySpark on Databricks
Databricks is the original creator of Spark and describes themselves as an "open and unified data analytics platform for data engineering, data science, machine learning and analytics." The company ad...
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Comprehensive Guide on Perceptrons in Machine Learning
A perceptron takes in multiple inputs and returns a single binary output. Visually, a perception can be depicted as follows: Note the following: our perceptron has $2$ inputs: $x_1$ and $x_2$ . In gen...
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