This file contains some central and important Machine Learning concepts.
- Supervised Learning: A type of machine learning where the algorithm is trained on a labeled dataset and the goal is to learn a mapping between the input features and the target variable.
- Unsupervised Learning: A type of machine learning where the algorithm is trained on an unlabeled dataset and the goal is to discover hidden patterns or relationships in the data.
- Semi-Supervised Learning: A type of machine learning that combines elements of supervised and unsupervised learning. It is often used when there is a large amount of unlabeled data and a small amount of labeled data available.
- Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or punishments.
- Overfitting: Occurs when a model is too complex and fits the training data too well, including the noise and random fluctuations. This can result in poor generalization to new data.
- Underfitting: Occurs when a model is too simple and cannot capture the underlying patterns in the data. This can result in poor performance on both the training and test data.
- Bias-Variance Tradeoff: A fundamental concept in machine learning that refers to the tradeoff between a model’s ability to minimize bias (the error due to incorrect assumptions) and variance (the error due to sensitivity to small fluctuations in the training data).
- Regularization: A technique used to prevent overfitting by adding a penalty term to the loss function that encourages simpler models.
- Cross-Validation: A technique used to assess the performance of a model by dividing the data into k folds and training and evaluating the model k times, each time using a different fold as the test set.
- Hyperparameter Tuning: The process of selecting the best hyperparameters for a machine learning algorithm by evaluating its performance on a validation set for different combinations of hyperparameter values.
- Linear Regression: Used for predicting a continuous target variable based on one or more predictor variables.
- Logistic Regression: Used for predicting the probability of an instance belonging to a particular class in classification problems.
- Decision Trees: Can be used for both classification and regression tasks. They work by recursively splitting the data based on the feature that results in the largest information gain.
- Random Forests: An ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
- Naive Bayes: A probabilistic classifier based on Bayes’ theorem. Often used for text classification and spam filtering.
- k-Nearest Neighbors (kNN): A non-parametric method used for classification and regression. It works by finding the k training examples closest to a new instance and using their labels to make a prediction.
- Support Vector Machines (SVM): Can be used for both classification and regression tasks. They work by finding the hyperplane that maximally separates the classes in the feature space.
- Neural Networks: Can be used for a wide range of tasks, including classification, regression, and clustering. They consist of layers of interconnected nodes that can learn complex relationships between the input and output data.
- Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while retaining as much information as possible.
- k-Means Clustering: An unsupervised learning algorithm used to partition data into k clusters based on their similarity.
- Data collection and preprocessing: Collect a dataset of images and their corresponding labels. Preprocess the data by resizing the images to a consistent size, normalizing the pixel values, and splitting the data into training and test sets.
- Feature extraction: Extract relevant features from the images that can be used as input to the machine learning algorithm. This can be done using techniques such as Principal Component Analysis (PCA) or by using a pre-trained neural network to extract features.
- Model selection and training: Choose a machine learning algorithm and train it on the extracted features and labels from the training set. This can involve tuning hyperparameters to achieve the best performance.
- Evaluation: Evaluate the trained model on the test set to assess its performance. This can involve calculating metrics such as accuracy, precision, recall, and F1-score.
- Prediction: Use the trained model to make predictions on new images.