Machine learning is changing the way we understand, utilize data and interpret across various industries. With a plethora of algorithms available, enthusiasts and data scientists can create models to solve complex problems, automate decision-making processes, and predict outcomes. This cheat sheet helps you to understand and implement popular Machine learning algorithms in your projects.

Supervised Learning Algorithms

Supervised learning algorithms learn predictions from labeled training data to make predictions on unseen data points. They are mostly used for classification, forecasting, and tasks such as regression.

1. Linear Regression: A simple algorithm-dependent variable and one or more independent variables that model the linear relationship.
2. Logistic Regression: Binary classification tasks used by a variation of linear regression, it models the probability of an event taking place based on the input features.
3. Decision Trees: A tree-like structure that recursively partitions data based on the most informative feature, resulting in a series of decisions that lead to a final prediction.
4. Random Forest: An aggregation method that builds multiple decision trees and combines their predictions through majority voting or averaging, improves generalization, and reduces overfitting.
5. K-Means Clustering: A partition-based clustering algorithm that groups data points into k clusters based on their similarity, minimizing the within-cluster sum of classes.

Unsupervised Learning Algorithms:

Unlike supervised learning, unsupervised learning involves using unlabeled data to identify patterns and relationships. Unsupervised learning algorithms are useful when there is no known outcome to predict, and the goal is to discover hidden structures in the data. Examples of unsupervised learning algorithms included association rule mining, dimensionality reduction, and clustering.

Hierarchical Clustering:  Builds a dendrogram by iteratively merging or splitting clusters with the method of tree-based clustering based on a distance metric and linkage criterion.

K-Means Clustering: A partition-based clustering algorithm that groups data points into k clusters based on their similarity, minimizing the within-cluster sum of classes.

Principal Component Analysis (PCA): A linear dimensionality reduction technique that projects data into a lower-dimensional subspace while preserving the maximum variance.

t-Distributed Stochastic Neighbor Embedding (t-SNE): A non-linear dimensionality decrease method that preserves local structures in the data, making it particularly suitable for seeing high-dimensional data.

Reinforcement Learning Algorithms:

Reinforcement learning is a type of ML algorithm that involves learning through trial and error. Neural networks are composed of stacked nodes that interact with one another to process and evaluate data, resulting in informed decisions. Reinforcement learning is used in a variety of applications, including robotics, gaming, and finance.

1. Q-Learning: A value-based reinforcement learning algorithm that estimates an action-value function to determine the optimal action in a given state.
2. Deep Q-Network (DQN): An extension of Q-learning that combines deep neural networks with reinforcement learning enables the algorithm to deal with complex problems with high-dimensional state spaces.
3. Policy Gradient Methods: A class of reinforcement learning algorithms that optimize policy directly by estimating the gradient of expected reward with respect to policy parameters.
4. Actor-Critic Methods: A hybrid approach that combines the strengths of the value-based and policy-based methods uses an actor network for policy learning and a critic network for estimating the value function.

Neural Networks and Deep Learning Algorithms:

Neural networks are a type of ML algorithm modeled after the structure and function of the human brain. Forecasting future trends, understanding natural language and speech recognition are all tasks that are made easier with AI. Deep learning is a subset of neural networks that involves using multiple layers of nodes to learn complex patterns and relationships in data. Deep learning is used in a wide variety of applications, including speech recognition, natural language processing, image, and autonomous driving.

1. Convolutional Neural Networks: A deep learning architecture specifically designed for processing grid-like data such as images by applying convolutional layers to detect local patterns and pooling layers to reduce spatial dimensions.
2. Recurrent Neural Networks (RNN): A class of neural networks that can process sequences of data while maintaining a hidden state that acts as a memory, making them suitable for time natural language processing, series forecasting, and speech recognition tasks.
3. Gated Recurrent Units: A simplified variant of LSTM that also learns long-term dependencies in sequence data but has fewer parameters and faster training times.
4. Autoencoders: A type of untrained neural network that learns to encode input data into a low-dimensional representation and then decodes it back to its original form, which is useful for dimensionality reduction, denoising, and anomaly detection.

Ensemble Learning Algorithms:

Ensemble learning is a technique used to improve the accuracy and robustness of ML algorithms by combining multiple models. Ensemble learning algorithms can be categorized into two types: bagging and boosting. Bagging involves training multiple models independently on different subsets of the data, and then combining their predictions. Ensemble learning algorithms are used in a variety of applications, including fraud detection, recommendation systems, and credit scoring.

1. Bagging: An aggregation method that trains multiple base models on random subsets of the training data with replacement by combining their predictions via majority voting or averaging.
2. Boosting: An iterative consolidation method that trains a sequence of weak learners, each focusing on correcting errors made by its predecessor, and combining their predictions in a weighted manner.
3. Stacking: An aggregated technique that trains multiple base models on the same dataset and uses their predictions as input features for a meta-model, which makes the final prediction.

Conclusion

Today we told you in this article what is Machine Learning: Unlocking the Potential of Data Analytics. With the help of machine learning, data analytics is turning into an invaluable tool. Computers can now learn from data & extract meaningful insights and even make accurate decisions autonomously. There are several types of ML algorithms, including supervised learning, unsupervised learning, reinforcement learning, neural networks, and ensemble learning. As data becomes increasingly abundant and complex, ML algorithms will continue to play an important role in unlocking the insights hidden within them.