Machine learning

Classical algorithms, model evaluation, feature work and the maths behind them.

All 501 notes, A to Z

Accelerate AI Training with Transfer Learning8 min
Transfer learning enables models to leverage knowledge from pre-trained networks, significantly reducing training time and computational resources. This...
Active Learning in Machine Learning with Python11 min
Active Learning is a machine learning paradigm where the algorithm can interactively query a user or other information source to obtain the desired outputs...
Active Learning vs Cooperative Learning in Python12 min
Active learning is a machine learning approach where the algorithm actively selects the most informative data points for labeling. This method aims to reduce...
Addressing Bias and Improving Model Performance7 min
Class imbalance occurs when the distribution of classes in a dataset is significantly skewed. This fundamental challenge in machine learning can severely...
Addressing Class Imbalance in Machine Learning11 min
Class imbalance occurs when dataset classes have significantly different sample sizes, affecting model performance. We'll implement a basic data analysis...
Advanced Integral Calculus in Machine Learning with Python7 min
Integral calculus plays a crucial role in various aspects of machine learning and artificial intelligence. It forms the foundation for many optimization...
Advantages of Transfer Learning in Python8 min
Transfer learning leverages knowledge gained from solving one problem and applies it to a different but related problem. This powerful approach enables...
Apriori Algorithm for Association Rule Mining6 min
The Apriori algorithm is a seminal technique for discovering frequent itemsets and generating association rules in transactional databases. It employs a...
Architectural Patterns for Machine Learning APIs9 min
Machine Learning SaaS APIs can be built using various architectural styles, each with its own strengths and use cases. This presentation explores six key...
Avoiding Pitfalls of Random Splitting in Machine Learning11 min
Random splitting of datasets is a common practice in machine learning, but it can lead to unexpected and potentially severe consequences if not done...
Avoiding Pitfalls of Random Splitting in ML Models9 min
Random splitting is a crucial technique in machine learning for dividing datasets into training and testing sets. However, when not handled properly, it can...
Bayes' Theorem in Machine Learning14 min
Bayes' theorem provides a mathematical framework for updating probabilities based on new evidence. In machine learning, it forms the foundation for...
Binomial Distribution in Machine Learning Using Python6 min
The binomial distribution models the number of successes in a fixed number of independent Bernoulli trials. It's characterized by two parameters: n (number...
Boosting Coffee Shop Sales with Statistical Power Analysis8 min
Statistical power analysis requires careful setup of experiment parameters and hypotheses testing. We'll create a class to handle the basic calculations for...
Boosting Machine Learning Efficiency with Vectorization11 min
Vectorization is a powerful technique in machine learning that transforms operations on individual data elements into operations on entire arrays or vectors....
Boosting Model Accuracy with Self-Training9 min
Semi-supervised learning (SSL) is a machine learning paradigm that leverages both labeled and unlabeled data. This approach is particularly useful when...
Boosting Python Efficiency with Asynchronous Programming7 min
Asynchronous programming in Python allows developers to write concurrent code that can handle multiple tasks efficiently. This paradigm is particularly...
Boosting PyTorch Model Inference Speed10 min
PyTorch is a powerful deep learning framework, but model inference can sometimes be slow. This presentation will explore various techniques to boost your...
Breast Cancer Survival Prediction with Machine Learning6 min
Breast cancer survival prediction using gene expression and clinical data is a crucial area of research in oncology. This presentation will guide you through...
Calculus and the Brachistochrone Problem in Machine Learning Using Python7 min
The Brachistochrone Problem, posed by Johann Bernoulli in 1696, seeks the curve of fastest descent between two points under gravity. This classical problem...
Causal Inference in Machine Learning in Python6 min
Causal inference is the process of determining the cause-and-effect relationships between variables in a dataset. It plays a crucial role in many fields...
Causal Learning Discovering New Machine Learning Frontiers17 min
Causal learning extends beyond traditional correlation-based machine learning by explicitly modeling cause-and-effect relationships between variables. This...
Choosing Appropriate Error Functions in Machine Learning10 min
Mean Squared Error (MSE) and Mean Absolute Error (MAE) are two common error functions used in machine learning. While MSE is often the default choice, it's...
Choosing Machine Learning Models for Small vs. Large Datasets14 min
When choosing a machine learning model, dataset size plays a crucial role. Smaller datasets often benefit from simpler models, while larger datasets can...
Choosing the Right Machine Learning Algorithm for Regression8 min
Regression analysis forms the foundation of predictive modeling, enabling us to understand relationships between variables and make quantitative predictions....
Comparing Machine Learning Techniques Transfer Fine-tuning Multitask Federated11 min
Transfer learning enables leveraging knowledge from a pre-trained model to solve a new but related task. This technique is particularly effective when...
Comparing Multiple Machine Learning Algorithms12 min
When developing machine learning solutions, it's crucial to explore various algorithms rather than settling on the first one that yields acceptable results....
Computational Graphs for Machine Learning in Python9 min
Computational graphs are powerful tools for representing and optimizing complex mathematical operations in machine learning. They form the backbone of modern...
Correlation, Regression, and Curve Fitting in Machine Learning with Python8 min
Correlation measures the strength and direction of the relationship between two variables. It's a fundamental concept in statistics and machine learning...
Data Flow in Machine Learning Projects8 min
Data collection is the foundation of any machine learning project. It involves gathering raw data from various sources such as APIs, databases, and web...