#1 What are the different types of Learning/ Training models in ML?

In machine learning, there are several types of learning or training models, each serving different purposes and applications. The main types include: Supervised Learning: Classification: The algorithm is trained on a labeled dataset to categorize input data into predefined classes or categories. Regression: The algorithm predicts a continuous output value based on input features. Unsupervised Learning: Clustering: Algorithms group similar data points into clusters without predefined labels. Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) reduce the number of features while retaining essential information. Semi-Supervised Learning: Combines elements of both supervised and unsupervised learning. The algorithm is trained on a dataset with both labeled and unlabeled data. Reinforcement Learning: An agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. Self-Supervised Learning: A type of unsupervised learning where the model generates its labels from the input data. For example, predicting missing parts of an image. Transfer Learning: Involves training a model on one task and then transferring the knowledge gained to a different but related task. This is often useful when labeled data for the target task is limited. Ensemble Learning: Combines multiple models to improve overall performance. Common techniques include bagging (e.g., Random Forests) and boosting (e.g., AdaBoost, Gradient Boosting). Deep Learning: Involves the use of neural networks with multiple layers (deep neural networks) to automatically learn hierarchical representations of data. Common architectures include Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data. Online Learning: The model is updated continuously as new data becomes available, allowing it to adapt to changing environments. Batch Learning: The model is trained on a fixed dataset, and updates are made in batches. These types of learning models cater to different problem domains and scenarios, providing flexibility in addressing a wide range of machine learning tasks. The choice of the learning model depends on factors such as the nature of the data, the task at hand, and the available resources. Read More... [Machine Learning Classes in pune](https://www.sevenmentor.com/machine-learning-course-in-pune.php) | [Machine Learning Course in Pune](https://www.sevenmentor.com/machine-learning-course-in-pune.php) | [Machine Learning Training in pune](https://www.sevenmentor.com/machine-learning-course-in-pune.php)