What Is MERN?

MERN Stack is a Javascript Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express, React and Node.js. It is designed to make the development process smoother and easier.

MongoDB

MongoDb is a NoSQL DBMS where data is stored in the form of documents having key-value pairs similar to JSON objects. MongoDB enables users to create databases, schemas and tables.

ExpressJS

ExpressJS is a NodeJS framework that simplifies writing the backend code. It saves you from creating multiple Node modules.



ReactJS

ReactJS is a JS library that allows the development of user interfaces for mobile apps and SPAs. It allows you to code Javascript and develop UI components.

NodeJS

NodeJS is an open-source Javascript runtime environment that allows users to run code on the server.

How Git Works - From Working Directory to Remote Repository

[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.

Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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