Machine Learning algorithms in one shot.

Overview of all important Machine Learning algorithms.

1. Introduction:

1. Machine learning algorithms enable computers to learn from data and make predictions or decisions.
2. They can be categorized into supervised learning, unsupervised learning, and reinforcement learning.

2. Supervised Learning Algorithms:

Regression: Predicts a continuous output variable based on input features.

1. Linear Regression
2. Polynomial Regression
3. Support Vector Regression
4. Decision Tree Regression
5. Random Forest Regression
6. Gradient Boosting Regression

Classification: Assigns input data to discrete categories or classes.

1. Logistic Regression
2. Naive Bayes
3. K-Nearest Neighbors (KNN)
4. Decision Trees
5. Random Forests
6. Support Vector Machines (SVM)
7. Gradient Boosting Classifiers
8. Neural Networks (Deep Learning)

3. Unsupervised Learning Algorithms:

Clustering: Groups similar data points based on their characteristics.

1. K-Means Clustering
2. Hierarchical Clustering
3. DBSCAN
4. Gaussian Mixture Models (GMM)

Dimensionality Reduction: Reduces the number of input features while preserving important information.

1. Principal Component Analysis (PCA)
2. Singular Value Decomposition (SVD)
3. t-Distributed Stochastic Neighbor Embedding (t-SNE)

Association Rule Learning: Identifies relationships and patterns in data.

1. Apriori Algorithm
2. Eclat Algorithm

4. Reinforcement Learning Algorithms:

1. Q-Learning: Learns an optimal policy through trial and error interactions with an environment.
2. Deep Q-Networks (DQN): Combines Q-Learning with deep neural networks.
3. Policy Gradient Methods: Optimizes policies by directly optimizing the expected reward.

5. Ensemble Learning Algorithms:

Bagging: Constructs multiple models using different subsets of the training data and combines their predictions.

1. Random Forests

Boosting: Builds models iteratively, giving more importance to misclassified instances.

1. AdaBoost
2. Gradient Boosting

Stacking: Combines predictions from multiple models using another model called a meta-learner.

6. Neural Network Architectures:

1. Feedforward Neural Networks
2. Convolutional Neural Networks (CNN)
3. Recurrent Neural Networks (RNN)
4. Long Short-Term Memory (LSTM)
5. Generative Adversarial Networks (GAN)

7. Evaluation Metrics:

1. Accuracy, Precision, Recall, F1 Score (for classification)
2. Mean Squared Error, Root Mean Squared Error (for regression)
3. Silhouette Score (for clustering)
4. Reward, Success Rate (for reinforcement learning)

8. Conclusion:

1. Machine learning algorithms offer a wide range of techniques to solve various tasks.
2. Understanding the characteristics and appropriate use cases of different algorithms is crucial for successful application in real-world scenarios.
3. Algorithm selection depends on the nature of the problem, available data, and desired outcome.