• Convolutional Neural Networks (CNNs) for image recognition, 
• Recurrent Neural Networks (RNNs), LSTM, GRU for sequence data, 
• Transformers (e.g., BERT, GPT) for language models, 
• Vision Transformers (ViT)

• Large Language Models (LLMs) like ChatGPT, BERT, and LLaMA  
• Text summarization, translation, sentiment analysis, 
• Prompt engineering and fine-tuning of LLMs

• Generative Adversarial Networks (GANs), 
• Variational Autoencoders (VAEs)  
• Text-to-image generation (e.g., DALL·E, Stable Diffusion)

• Deep Reinforcement Learning (e.g., DQN, PPO)  
• Applications in robotics, gaming (e.g., AlphaGo), and finance

• Model deployment and monitoring, 
• Continuous integration/continuous deployment (CI/CD) for ML  
• Model versioning and reproducibility  

• Model interpretability and transparency: 
• SHAP, LIME, and other tools  
• Fairness, accountability, and ethical AI  

• Learning from unlabeled data, 
• Contrastive learning methods (e.g., SimCLR, BYOL) 
• Clustering and dimensionality reduction  

• Pre-trained models adapted to new tasks. 
• Especially common in NLP and computer vision

• Linear Regression: Used for predicting continuous outcomes. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
• Logistic Regression: Used for binary classification tasks (e.g., predicting yes/no outcomes). It estimates probabilities using a logistic function.
• Decision Trees: These models predict the value of a target variable by learning simple decision rules inferred from the data features.
• Random Forests: An ensemble of decision trees, typically used for classification and regression, improving model accuracy and overfitting control.
• Support Vector Machines (SVM): Effective in high-dimensional spaces, SVM is primarily used for classification but can also be used for regression.

• Clustering: Algorithms like K-means, hierarchical clustering, and DBSCAN group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
• Association: These algorithms find rules that describe large portions of your data, such as market basket analysis.
• Principal Component Analysis (PCA): A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.
• Autoencoders: A Special type of neural network used to learn efficient codings of unlabeled data.

Reinforcement Learning

• Q-learning: This is a model-free reinforcement learning algorithm that learns the value of an action in a particular state.  
• Deep Q-Networks (DQN): It combines Q-learning with deep neural networks, allowing the approach to learn successful policies directly from high-dimensional sensory inputs.  
• Policy Gradient Methods: These methods optimize the parameters of a policy directly as opposed to estimating the value of actions.  
• Monte Carlo Tree Search (MCTS): Used in decision processes for finding optimal decisions by playing out scenarios, notably used in games like Go.  

• Generative adversarial networks: GANs are an important type of deep learning algorithm. They rely on multiple neural networks, which compete with each other to produce more desirable new data based on existing data. One network, the generator, creates new output, while the second, the discriminator, tries to determine whether the new data is real or AI-generated. Over time, the generator's ability to create original data improves until the discriminator can no longer distinguish the new data from the original data. 
• Transformers: These models employ a mathematical technique called self-attention, along with a neural network, to identify context and establish relationships between data points. Transformers are foundational to several AI applications, including text-to-speech conversion and drug research based on understanding gene sequences in DNA. 
• Diffusion: Diffusion models create new data based on their training data. It's diffusion that enables generative AI to, for example, create an image in the style of a master like Picasso. In effect, diffusion first reduces an original image into a set of pixels, and then reconstructs an image using elements and styles prompted by the user. Autoregressive models.
•  Autoregressive models: automatically predict the next element of a sequence by examining previous, or regressive, elements of the sequence. These models are well-suited to time-related events such as stock price forecasting, weather prediction, and traffic prediction. 
• Large language models: Popular generative AI platforms like ChatGPT use LLMs to interpret user queries, known as prompts, and then generate sophisticated text, images, and even software code in response. 
• Variational autoencoders: VAEs are an unsupervised neural network consisting of an encoder and a decoder. The encoder learns to simplify data and capture just the essential features of the input. The decoder takes the simplified input and reverses the process, which creates something new -- resembling examples from the training data set.
• Recurrent neural networks: RNNs are deep learning models designed to convert input data into a specific output. By using the same parameters for each input, it can reduce the complexity of the parameters. RNNs are well suited to language translation, speech recognition and other natural language processing tasks. 
• Multimodal AI: Multimodal AI models can interpret multiple types of data, such as images, text, audio, and video. For example, a multimodal model could generate a video with background music based on a textual prompt.