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Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Data Imbalance Handling

Dimensionality Reduction Methods

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Multiclass Classification Strategies

Natural Language Processing Terminology

Model Selection Criteria

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Machine Learning Pipelines

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Data Collection

The process of gathering data from various sources which will be used for training the machine learning model. This stage sets the foundation for the quality and quantity of the data that will be processed and analyzed.

Data Cleaning

Involves preprocessing data to remove inaccuracies, duplicate values, and irrelevant information. Data cleaning ensures the model learns from high-quality, relevant data.

Model Evaluation

Assessing the model's performance using various metrics, like accuracy, precision, recall, and F1 score, to understand how well the model predicts on the validation set and to tune it before real-world deployment.

Model Selection

The process where different machine learning algorithms are considered and evaluated to choose the best one based on performance metrics for the specific problem at hand.

Feature Engineering

The practice of selecting, modifying, or creating relevant features from raw data to enhance the performance of machine learning models. This step can greatly influence the model's predictive power.

Model Training

The phase in which a machine learning model learns from the training data by adjusting its parameters to minimize a loss function, with an aim of making accurate predictions.

Data Labeling

The process of identifying raw data and adding one or more meaningful and informative labels to provide context that a machine learning model can learn from, crucial for supervised learning tasks.

Data Splitting

Dividing the dataset into separate sets, typically including a training set, validation set, and test set, to train the model and evaluate its performance in an unbiased manner.

Model Retraining

The process of updating a machine learning model with new data to maintain its accuracy and to account for any changes in the underlying patterns since it was last trained.

Model Deployment

The stage where the trained model is integrated into a production environment to make real-time predictions or insights based on new data. Deployment can vary in complexity depending on the requirements.

Monitoring and Maintenance

Ongoing tracking of the model's performance in production to ensure it maintains high accuracy and relevance, with updates and adjustments made as necessary.

Hyperparameter Tuning

The process of optimizing the hyperparameters of a model, which are not learned during training, to improve performance. Techniques such as grid search or random search are often used.

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