Machine learning
Classical algorithms, model evaluation, feature work and the maths behind them.
Page 2 of 17
- Derivatives in AI and Machine Learning Algorithms10 min
Derivatives are indeed fundamental to many AI and Machine Learning algorithms. This mathematical concept from calculus plays a crucial role in optimizing...
- Diagnosing Data Deficiency in Machine Learning Models6 min
Data deficiency in machine learning models can significantly impact performance. This technique helps determine if more data will improve your model or if...
- Differences Between Pretraining Finetuning and Transfer Learning11 min
Pretraining is a fundamental technique in machine learning where a model is trained on a large dataset to learn general features and patterns before being...
- Differential Geometry in Machine Learning with Python11 min
Differential geometry provides a powerful framework for understanding and optimizing machine learning algorithms. It allows us to analyze the geometric...
- Double and Triple Integrals for Machine Learning and AI6 min
Introduction to Double and Triple Integrals in Machine Learning and AI
- Explaining Pretraining, Finetuning, and Transfer Learning in Machine Learning12 min
Pretraining, finetuning, and transfer learning are essential techniques in machine learning, particularly in deep learning. These methods allow us to...
- Exploring Correlations in Machine Learning Using Python8 min
Correlation is a statistical measure that quantifies the relationship between two variables. In machine learning and AI, understanding correlations is...
- Exploring Popular Distance Measures in Machine Learning8 min
The Euclidean distance represents the shortest straight-line path between two points in a multi-dimensional space. It's calculated as the square root of the...
- Exploring the Mathematics of Machine Learning Cost Functions10 min
The cost function is a crucial component in machine learning, measuring how well a model's predictions align with actual values. Let's explore the...
- Exploring Transcendence in Machine Learning with Python9 min
Transcendence in machine learning refers to the ability of AI systems to surpass human-level performance in specific tasks. This concept explores the...
- Extracting Data from Images using Python Cross-Validation8 min
Cross-validation is a crucial technique in machine learning for assessing model performance and preventing overfitting. It involves splitting the dataset...
- Federated Learning with Python Decentralized Machine Learning7 min
Federated Learning is a machine learning technique that trains algorithms across decentralized devices or servers holding local data samples, without...
- Generative vs Discriminative Machine Learning Models9 min
Discriminative models directly learn the mapping between input features and output labels by modeling the conditional probability P(Y|X). These models focus...
- Generative vs Discriminative Models in Machine Learning9 min
Generative models aim to learn the joint probability distribution P(X,Y) of input features X and labels Y, enabling them to generate new samples by sampling...
- Implementing Machine Learning Algorithms from Scratch in Python12 min
Ensemble learning is a powerful machine learning technique that combines multiple models to improve overall performance. By leveraging the strengths of...
- Improving Unsatisfactory Model Performance with Python10 min
After training a machine learning model, it's crucial to evaluate its performance. If the metrics are unsatisfactory, there are several steps you can take to...
- Interpreting Machine Learning Models with Partial Dependence Plots8 min
Partial Dependence Plots (PDP) visualize the marginal effect of a feature on model predictions while accounting for the average effect of other features....
- Key Data Science Techniques! Classification, Clustering, Regression8 min
Data science employs various techniques to analyze and predict outcomes from data. Three fundamental approaches are classification, clustering, and...
- Lagrange Multipliers in Machine Learning and AI:9 min
Lagrange Multipliers are a powerful mathematical tool used in optimization problems, particularly in machine learning and AI. They help us find the optimal...
- Levels of Measurement in Machine Learning with Python8 min
Levels of measurement, also known as scales of measurement, are fundamental concepts in statistics and machine learning. They categorize data based on the...
- Location Parameter in Machine Learning with Python8 min
The location parameter is a crucial concept in machine learning, particularly in statistical modeling. It represents the central tendency of a distribution...
- Loss and Cost Functions in Machine Learning with Python8 min
Loss and cost functions are fundamental concepts in machine learning, guiding the learning process of algorithms. While often used interchangeably, they have...
- Manim for Machine Learning in Python9 min
Manim is a powerful animation library in Python, originally created by Grant Sanderson (3Blue1Brown) for mathematical animations. It has since evolved to...
- Mastering the Machine Learning FTI Pipeline in Python13 min
The Feature, Training, and Inference (FTI) Pipeline forms the backbone of robust machine learning systems. This pipeline encompasses the entire process from...
- Neural Scaling in Python Optimizing Model Performance7 min
Neural scaling refers to the practice of increasing the size and complexity of neural networks to improve their performance. This concept has gained...
- Popular Distance Measures in Machine Learning6 min
Distance measures play a crucial role in various machine learning algorithms, helping to quantify the similarity or dissimilarity between data points. These...
- Power Series and Sequences in Machine Learning Using Python8 min
Power series are mathematical tools that represent functions as infinite sums of terms. In machine learning, they're used to approximate complex functions...
- Predicting Olympic Outcomes with Python and Machine Learning7 min
Predicting Olympic outcomes using machine learning and Python is an exciting application of data science in sports. This process involves collecting...
- Predictive Maintenance and Condition-Based Monitoring Using Machine Learning13 min
Predictive Maintenance and Condition-Based Monitoring (CBM) are advanced approaches to equipment maintenance that leverage data analytics and machine...
- Probability Fundamentals for Machine Learning13 min
Conditional probability forms the foundation of many machine learning algorithms, particularly in classification tasks. It represents the probability of an...