Machine Learning 101: Everything You Need to Know About It
Tags: machinelearning, beginners, ai
Published: 2023-04-05 08:42:23

_This blog post is adapted from the blog post "[Introduction to MLOps](https://www.infracloud.io/blogs/introduction-to-mlops/)" on InfraCloud's blog written by me._

As commonly understood, machine learning is a subset of artificial intelligence that enables computers to learn and make predictions without requiring explicit programming.

Machine learning finds widespread usage across diverse industries. Its applications range from providing personalized movie recommendations such as on YouTube or Netflix to recognizing your voice commands and activating a fan like Alexa. 

Even sectors like banking rely on it to identify instances of credit card fraud and introduce chatbots to enhance customer service speed. These examples are just a few instances of how machine learning is employed in everyday life.


![Machine Learning 101: Everything You Need to Know About It](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/32ra2cetioroqk3rm3c7.png)



### Types of Machine Learning
Similar to how humans learn in various ways, there exist techniques by which algorithms can also learn. The following are the three primary categories of machine learning:

- **Supervised**: Supervised learning involves labeled input data, where certain data points are already tagged with the correct answer. For instance, when working with input data comprising images of fruits, each image would be labeled with the corresponding fruit. From this, the model learns and can produce accurate output for new data based on the labeled data. Regression and classification are two types of supervised learning models.

- **Unsupervised**: Unsupervised learning refers to input data that is neither labeled nor classified. The algorithm must analyze this data without any prior information. The model must group unsorted input based on patterns and similarities. As an example, you could provide input consisting of unlabeled images of fruits that the algorithm has never seen before. The algorithm will categorize the fruits into groups based on the patterns it identifies. Clustering and Association are two types of unsupervised learning models.

- **Reinforcement**: Reinforcement learning works based on feedback. The algorithm learns to perform actions in an environment and receives feedback based on those actions. It operates using the trial-and-error method, and over time, the model learns to behave in the environment.

### Machine learning models

Machine learning models are at the core of machine learning. These algorithms are trained on datasets and fine-tuned to provide accurate predictions for new data. The outcome of this process is a machine learning model.

There exist various types of machine learning models based on the approach they use to predict, including:

* **Regression models:** Machine learning models that forecast numerical values are designed to identify relationships between dependent and independent variables to make predictions. A regression model is an instance of this type of model. For example, anticipating the stock price using historical data is a regression model. Linear regression stands out as one of the most commonly employed regression models.

![Linear Regression. Courtesy: Spiceworks](https://pimages.toolbox.com/wp-content/uploads/2022/04/07040339/25-4.png "Linear Regression. Courtesy: Spiceworks")

* **Classification models:** Machine learning models that forecast categorical values are designed to recognize patterns in data and classify them into specific categories. A classification model is an instance of this type of model. For example, classifying emails as spam or non-spam is accomplished using a classification model. Decision trees and random forests are two popular types of classification models.


![Classification model. Courtesy: Spiceworks](https://pimages.toolbox.com/wp-content/uploads/2022/09/02134804/Diagram-depicting-SVM-example-with-hyperplane-for-classification-problem.jpg "Classification model. Courtesy: Spiceworks")


* **Clustering models:** Clustering models group data points together by identifying patterns and grouping similar data points into clusters. These models function by grouping data points together based on their similarities. K-Means is an example of a well-known clustering model. For instance, clustering models are used to group customers based on their purchase history.

![Clustering models. Courtesy: DataFlair](https://data-flair.training/blogs/wp-content/uploads/sites/2/2019/08/clustering-in-machine-learning.png "Clustering models. Courtesy: DataFlair")


* **Neural Network Models:** Neural network models are utilized for intricate tasks, including speech and vision, inspired by the human brain. These models operate by leveraging layers of artificial neurons to detect complex patterns in data. Convolutional Neural Network is one of the most widely employed neural network models and is extensively used for image recognition.

![Neural network model. Courtesy: Otexts](https://otexts.com/fpp2/nnet2.png "Neural network model. Courtesy: Otexts")


## Machine Learning Development: Existing Challenges

Despite the progress made in the Artificial Intelligence (AI) and machine learning field, the advancement of associated processes has been slow-paced. Many organizations currently utilize machine learning models for various use cases; however, most of these processes are manual and present several challenges.

- **Data quality and management:** The performance of a machine learning model can be negatively impacted by incomplete or incorrect data. Working manually with various data sources and formats is prone to errors and can impede the process.

- **Model complexity:** Over time, machine learning models can become intricate due to evolving data and environments. The deployment, scaling, and management of complex models can be challenging.

- **Reproducibility:** Recreating machine learning models manually and monitoring changes over time can be challenging due to differences in data, environments, infrastructure, and other factors.

- **Deployment Complexity:** The deployment of machine learning models across diverse environments and systems can be challenging and may necessitate considerable modifications to your infrastructure.

- **Collaboration:** When it comes to machine learning development teams, individuals with diverse expertise, such as data scientists, developers, and operations staff, are typically involved. Without a cohesive process in place, these three groups may work independently, leading to communication gaps and misunderstandings.

MLOps was introduced to automate and streamline various processes that hamper the development and deployment of machine learning models. 

By overcoming challenges like incomplete or incorrect data, working with multiple data sources and formats, deploying and scaling complex models, reproducing models in different environments and systems, and siloed teams, MLOps has become a game-changer for the industry. 

You can read the complete blog post on [Introduction to MLOps](https://www.infracloud.io/blogs/introduction-to-mlops/).

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