- Supervised Learning Techniques: These involve training a model on a labeled dataset, where the correct output values are known.
- Linear Regression: It is used for predicting continuous outcomes, such as predicting housing prices based on various features of the house.
- Logistic Regression: It is a classification algorithm that is used for predicting binary outcomes, such as whether an email is spam or not.
- Support Vector Machines (SVMs): These can be used for both regression and classification tasks but are typically used for classification.
- Decision Trees and Random Forests: These are used for both regression and classification tasks, and they are particularly good at handling tabular data with numerical features or categorical features.
- Gradient Boosting Machines (GBMs): GBMs, including XGBoost and LightGBM, are powerful techniques for both regression and classification problems.
- Neural Networks: These are used for both regression and classification tasks, and they are particularly good at handling high-dimensional and complex data such as images, audio, and text.
2. Unsupervised Learning Techniques: These involve training a model on an unlabeled dataset, where the correct output values are not known.
- K-means Clustering: This is used to group similar data points together based on their features.
- Hierarchical Clustering: This is another method for grouping data points together, but it can also create a tree-based hierarchical relationship between the groups.
- Principal Component Analysis (PCA): This is used to reduce the dimensionality of a dataset by creating new features that maximize the variance in the data.
- Autoencoders: These are neural networks that are used to learn compact and useful representations of the data in an unsupervised way.
3. Reinforcement Learning Techniques: These involve training a model to make a sequence of decisions, where the model learns by receiving feedback in terms of rewards or penalties.
- Q-learning: This is a technique for learning a policy that tells an agent what action to take under what circumstances.
- Deep Q Networks (DQNs): This is an extension of Q-learning that uses a neural network to approximate the Q-value function.
- Policy Gradients: This is another technique for learning a policy, where the policy is directly parameterized and learned.
4. Deep Learning Techniques: A subset of machine learning, these are primarily used for tasks that benefit from neural networks.
- Convolutional Neural Networks (CNNs): These are typically used for image classification tasks.
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks: These are used for tasks that involve sequential data, such as time series analysis or natural language processing.
- Transformers: Introduced in the "Attention is All You Need" paper, they have become especially popular in natural language processing tasks, outperforming RNNs and LSTMs in many cases.
