46 docs tagged with "machine-learning"

Understanding the most common evaluation metric, its formula, and its fatal flaws in imbalanced datasets.

Combining value-based and policy-based methods for stable and efficient reinforcement learning.

Neural network-based dimensionality reduction: Encoder-Decoder architecture and bottleneck representations.

Discovering clusters of arbitrary shapes and identifying outliers using density-based spatial clustering.

Understanding recursive partitioning, Entropy, Gini Impurity, and how to prevent overfitting in tree-based models.

Scaling Reinforcement Learning with Deep Learning using Experience Replay and Target Networks.

Combining L1 and L2 regularization for the ultimate balance in feature selection and model stability.

Mastering the harmonic mean of Precision and Recall to evaluate models on imbalanced datasets.

Mastering the techniques used to harmonize feature scales, ensuring faster convergence and better model accuracy.

Techniques for identifying and keeping only the most relevant features using filter, wrapper, and embedded methods.

Probabilistic clustering using Expectation-Maximization and the Normal distribution.

Exploring the power of Sequential Ensemble Learning, Gradient Descent, and popular frameworks like XGBoost and LightGBM.

Understanding Agglomerative clustering, Dendrograms, and linkage criteria.

Mastering robust model evaluation by rotating training and testing sets to maximize data utility.

Grouping data into K clusters by minimizing within-cluster variance.

Understanding the proximity-based classification algorithm: distance metrics, choosing K, and the curse of dimensionality.

Understanding L1 regularization, sparse models, and automated feature selection.

The most exhaustive validation technique: training on N-1 samples and testing on a single observation.

Mastering the fundamentals of predicting continuous values using lines, slopes, and intercepts.

How to use Scikit-Learn's built-in datasets, fetchers, and external loaders to prepare data for modeling.

Understanding cross-entropy loss and why it is the gold standard for evaluating probability-based classifiers.

Understanding binary classification, the Sigmoid function, and decision boundaries.

How to use trained Scikit-Learn estimators to generate point predictions and probability estimates.

How to choose the right algorithm, split data correctly, and use Cross-Validation to ensure model reliability.

A deep dive into Min-Max scaling, MaxAbs scaling, and Unit Vector normalization for bounded data ranges.

Optimizing the policy directly: understanding the REINFORCE algorithm, stochastic policies, and the Policy Gradient Theorem.

Learning to model curved relationships by transforming features into higher-degree polynomials.

Understanding Precision, its mathematical foundation, and why it is vital for minimizing False Positives.

Mastering feature extraction, variance preservation, and the math behind Eigenvalues and Eigenvectors.

Mastering the Bellman Equation, Temporal Difference learning, and the Exploration-Exploitation trade-off.

Understanding Ensemble Learning, Bagging, and how Random Forests reduce variance to build robust classifiers.

Understanding Recall, its mathematical definition, and why it is critical for minimizing False Negatives.

Understanding the Agent-Environment loop, reward signals, and how AI learns to make optimal decisions in dynamic systems.

Mastering L2 regularization to prevent overfitting and handle multicollinearity in regression models.

Evaluating classifier performance across all thresholds using the Receiver Operating Characteristic and Area Under the Curve.

How AI learns by predicting missing parts of its own input, powering Large Language Models and Computer Vision.

Combining small amounts of labeled data with large amounts of unlabeled data to improve model accuracy and reduce labeling costs.

A deep dive into supervised learning: regression, classification, and the relationship between features and targets.

Mastering the geometry of classification: margins, hyperplanes, and the Kernel Trick.

The foundation of classification evaluation: True Positives, False Positives, True Negatives, and False Negatives.

Mastering the data partitioning process to ensure unbiased model evaluation.

Discovering patterns in unlabeled data through clustering, association, and dimensionality reduction.

A comprehensive introduction to the Machine Learning Tutorial structure, purpose, and key learning outcomes for CodeHarborHub learners.

Define Machine Learning, its key characteristics, and how it differs from traditional programming.

Understanding the paradigm shift from traditional programming to data-driven learning.

Understanding the difference between training performance and real-world reliability.