Machine learning
Classical algorithms, model evaluation, feature work and the maths behind them.
Page 12 of 17
- Neural Nets vs Decision Trees in Python8 min
Neural networks and decision trees are both powerful machine learning algorithms used for classification and regression tasks. While they operate on...
- Part 1: Foundations of Machine Learning43 min
- Popular Machine Learning Models7 min
Machine learning models are algorithms that learn patterns from data to make predictions or decisions. They form the backbone of artificial intelligence...
- Preparing for a Machine Learning Interview11 min
Machine learning is a vast field with numerous concepts to grasp. Understanding the fundamental principles is crucial for success in interviews and practical...
- Seven Most Used Machine Learning Algorithms11 min
Machine learning engineers rely on a set of core algorithms to solve various problems. This presentation will cover seven fundamental algorithms: Logistic...
- Supervised Machine Learning Concepts and Techniques22 min
Supervised Machine Learning is a fundamental concept in artificial intelligence where an algorithm learns from labeled training data to make predictions or...
- TanH Function in Machine Learning with Python8 min
The TanH (Hyperbolic Tangent) function is a crucial activation function in neural networks and machine learning. It maps input values to output values...
- The time complexity of 10 popular ML algorithms7 min
The time complexity of machine learning algorithms is crucial for understanding their efficiency and scalability. This presentation will cover the time...
- Top 10 Machine Learning Algorithms28 min
The Naïve Bayes classifier implements Bayes' theorem with strong independence assumptions between features. Despite its simplicity, it often performs...
- Top Machine Learning Algorithms Explained14 min
Linear regression serves as a foundational predictive modeling algorithm that establishes relationships between dependent and independent variables through a...
- Transitioning Engineers to Machine Learning14 min
Engineers can indeed transition into the machine learning field, regardless of their background. This transition requires dedication, time, and continuous...
- Understanding and Addressing Class Imbalance9 min
Class imbalance occurs when one class in a dataset significantly outnumbers others. This phenomenon is common in real-world scenarios, such as fraud...
- Understanding Cross-Validation in Python10 min
Cross-validation is a statistical method used to assess model performance by partitioning data into training and testing sets multiple times. It helps...
- Understanding Machine Learning Algorithms14 min
Linear regression serves as a foundational supervised learning algorithm for predicting continuous values. It establishes relationships between independent...
- Understanding Machine Learning Algorithms and Time Complexity in Python11 min
Machine learning algorithms are computational methods that enable systems to learn and improve from experience without being explicitly programmed. Time...
- Understanding the 3 Main Types of Machine Learning24 min
Linear regression serves as a foundational supervised learning algorithm that models the relationship between dependent and independent variables. This...
- Understanding the 3 Types of Machine Learning in Python9 min
Machine Learning is a subset of Artificial Intelligence that focuses on developing algorithms and models that enable computers to learn from data and improve...
- Understanding the Gini Index From Inequality to Machine Learning9 min
The Gini Index serves as both an inequality measure and a classification metric in machine learning. In its statistical form, it quantifies the disparity...
- x## Machine Learning Fundamentals Hinge Loss and Optimization14 min
Hinge loss is a fundamental loss function used in Support Vector Machines and other classification tasks. It penalizes predictions based on their distance...
- Akaike's Information Criterion (AIC) for Model Selection9 min
Akaike's Information Criterion (AIC) is a powerful tool in statistics and probability for model selection and comparison. Developed by Japanese statistician...
- Avoiding Data Leakage in Cross-Validation8 min
Data leakage occurs when information from the validation set influences model training, leading to overoptimistic performance estimates. This common pitfall...
- Bayesian Information Criterion for Model Selection14 min
The Bayesian Information Criterion provides a mathematical framework for model selection based on the trade-off between model complexity and goodness of fit....
- Calculating Confusion Matrix Metrics7 min
In binary classification, a confusion matrix is a 2x2 table that visualizes the performance of a model by comparing predicted values against actual values....
- Calculating Metrics from Confusion Matrix4 min
A confusion matrix is a fundamental tool in machine learning that tabulates predicted versus actual values, enabling evaluation of classification model...
- Calculating Precision from Confusion Matrix in Machine Learning10 min
A confusion matrix serves as the foundation for calculating precision and other key performance metrics in binary classification. It represents a structured...
- Components of a Confusion Matrix in Binary Classification7 min
The confusion matrix serves as a fundamental evaluation metric in binary classification, comprising four essential components that measure the alignment...
- Comprehensive Guide to Cross-Validation in Machine Learning11 min
Cross-validation is a crucial technique in machine learning that helps assess a model's performance and generalization ability. It involves splitting the...
- Conformal Prediction Measuring ML Model Confidence14 min
Conformal prediction is a statistical method that provides a measure of confidence for individual predictions in machine learning models. Unlike traditional...
- Conformal Predictions in Machine Learning with Python17 min
Conformal prediction is a framework for making reliable predictions with a guaranteed error rate. It provides a way to quantify uncertainty in machine...
- Confusion Matrix and Performance Metrics in Python10 min
A confusion matrix is a fundamental tool in machine learning for evaluating classification models. It provides a tabular summary of the model's predictions...