# ML Algorithms and Models

**- Overview**

Machine learning (ML) involves the use of ML algorithms and models.

ML algorithms are processes implemented in code and run on data. The ML model is output by the algorithm and consists of model data and prediction algorithm.

ML algorithms provide a type of automated programming in which ML models represent programs.

Simply put, a ML algorithm is like a recipe that allows computers to learn and predict based on data. Rather than explicitly telling the computer what to do, we feed it large amounts of data and let it discover patterns, relationships, and insights on its own.

ML algorithms are sets of rules or processes used by an AI system to perform tasks. These tasks often involve discovering new data insights and patterns, or predicting output values from a given set of input variables.

Some applications of machine learning include:

- Image recognition
- Speech recognition
- Recommender systems
- Fraud detection
- Self driving cars
- Medical diagnosis
- Stock market trading
- Virtual try on

**- Machine Learning Algorithms**

ML algorithms are the brains behind any model, allowing machines to learn, making them smarter. The way these algorithms work is that they are fed an initial batch of data and, over time, additional data is fed as the algorithm improves its accuracy.

This process of regularly exposing algorithms to new data and experiences increases the overall efficiency of the machine.

Here are some ML algorithms:

- Reinforcement learning: Helps software agents and machines automatically evaluate the best behavior in a given environment.
- Supervised learning: Provides the model with labeled data to train on. The model learns how to make correct predictions.
- Semi-supervised learning: Uses a combination of labeled and unlabeled data sets during the training period.

- Unsupervised learning: Learns the underlying structure of a dataset without being provided a target variable.
- Deep learning: Uses different layers of non-linear processing units for feature extraction.

- Nearest neighbor: Classifies data points based on their similarity to other data points.

- Gradient boosting: Relies on the idea that the best possible next model, when combined with previous models, minimizes the overall prediction error.
- Apriori: Generates association rules for a given set of data.

When choosing a ML algorithm, you can consider:

- Your project goal
- Your data's size, processing, and annotation requirements
- The speed and training time
- Your data's linearity
- The number of features and parameters

To assess the performance of ML algorithms, it is essential to establish evaluation criteria. These criteria typically include accuracy, precision, recall, F1-score, training time, model complexity, and interpretability.

**- Machine Learning Models**

A ML model is a program that uses computational methods to make predictions or find patterns in a dataset. ML models are mathematical representations of the output of the training process.

ML models are built using supervised ML algorithms that use labeled, unlabeled, or mixed data. They are similar to mathematical functions, taking input data, making a prediction, and serving a response.

ML models are different from rule-based programs because they don't need to be explicitly coded and can change over time as new data enters the system.

Here are some steps for implementing a basic ML algorithm:

- Define the problem and gather data
- Preprocess and explore the data
- Split the data into training and testing sets
- Choose a basic ML algorithm
- Train the model
- Deploy the model

**- Model Training**

Model training is a stage in the data science development lifecycle. It's the process of running a ML algorithm on a dataset, and then optimizing the algorithm to find certain patterns or outputs.

Model training involves learning good values for all the weights and bias from labeled examples. The resulting function with rules and data structures is called the trained ML model.

The process of training ML models can be divided into four steps:

- Data set split for training and evaluation
- Algorithm selection
- Hyperparameter tuning
- Model training

The model's performance during training will eventually determine how well it will work when it is eventually put into an application for the end-users.

Before training your model, you can:

- Identify the problem and candidate algorithms.
- Identify data required to train the algorithms.
- Collect initial data.
- Identify its quality and suitability for the task.
- Plan what is needed to make the dataset suitable for the project.

### - Top ML Algorithms

Here are some machine learning algorithms:

- K-means clustering: A clustering algorithm that groups similar data points into clusters. The number of groups is called K.
- Reinforcement learning: A machine learning algorithm where a machine learns ideal behavior to maximize its performance.
- Supervised learning: A machine learning algorithm that uses a known dataset to make predictions.
- K-Nearest Neighbor (KNN): A machine learning algorithm that solves classification problems by assigning a new data point to a category.
- Logistic regression: A machine learning classification algorithm that predicts the probability of certain classes based on dependent variables.
- Support Vector Machines (SVMs): A supervised learning algorithm that is used for classification, regression, and outlier detection tasks.
- Naive Bayes: A machine learning algorithm based on the idea of using Bayes' theorem to make predictions.
- Random forest: A supervised learning algorithm that is used for classification and regression.
- Decision tree: A machine learning algorithm for classification and regression problems.

**[More to come ...]**