Understanding the Confusion Matrix in Machine Learning
In machine learning, particularly in the field of classification, the confusion matrix is a useful tool for evaluating the performance of a binary classifier.
Machine Learning
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Understanding the Bias-Variance Trade-Off in Machine Learning
What is Bias-Variance Trade-Off? In the world of machine learning, the bias-variance trade-off is one of the most crucial concepts for building a successful model. It represents the delicate balance between two types of errors that can influence the performance of a model: bias and variance. These two sources of error can be thought of…
What is Data Science? Understanding the Differences Between Supervised and Unsupervised Learning
What is Data Science? Data Science is an interdisciplinary field that blends various tools, algorithms, machine learning principles, and statistical techniques with the ultimate goal of extracting valuable insights from raw data. The primary focus of data science is to analyze large and complex data sets to uncover patterns, trends, and relationships that can…
Exploring Non-Gaussian Data Sets: Understanding Binomial, Poisson, and More
What is an Example of a Data Set with a Non-Gaussian Distribution? In the world of machine learning and statistics, the Gaussian distribution (also known as the normal distribution) is one of the most commonly used distributions. This distribution is often assumed for many algorithms because it has nice mathematical properties, such as being symmetric…
Understanding Selection Bias: How It Impacts Data Analysis and Modeling
What is Selection Bias and How Does It Affect Data Analysis? In data analysis and machine learning, one of the most significant challenges is ensuring that the data used for training models is representative of the population or real-world scenarios the model will encounter in the future. If the data is skewed, incomplete, or unrepresentative,…
Key Assumptions for Linear Regression: Ensuring Model Validity
Understanding the Assumptions of Linear Regression Linear regression is a powerful statistical technique used for modeling the relationship between a dependent variable and one or more independent variables. While the model can be highly effective for making predictions, its validity and accuracy depend on certain assumptions being met. These assumptions ensure that the model fits…
Understanding Linear Regression and Key Metrics: P-Value, Coefficients, and R-Squared
What is Linear Regression? Linear regression is one of the simplest and most widely used statistical methods in predictive modeling. It is a technique used to understand the relationship between a dependent variable (also called the target or output) and one or more independent variables (also known as predictors or features). The goal is to…
How LLMs Work: Step-by-Step Explanation
What is a large language model (LLM)? Large Language Models are machine learning models that employ Artificial Neural Networks and large data repositories to power Natural Language Processing (NLP) applications. An LLM serves as a type of AI model designed to be able to grasp, create, and manipulate natural language. These models rely on deep…
How a Large Language Model (LLM) predicts the next word
How a Large Language Model (LLM) predicts the next word, including all the mathematical operations involved at each step, with the appropriate vector and tensor manipulations.
Large Language Models
Course on Large Language Models NOTE: You’re only meant to change code marked with “# TODO:” Table of Contents Setting Up API Key Configuration Connecting to OpenAI API Exploring the API Creating Chat Completions Understanding Completion Parameters Prompt Engineering Crafting Effective Prompts Strategies and Best Practices Advanced Techniques Utilizing Embeddings Function Calling in LLMs Extras…