Machine Learning is the subdomain of AI (Artificial Intelligence). It is a rapidly growing field, and unknowingly we are using it in our day-to-day life as Amazon’s Alexa, google assistant etc. Machine learning refers to developing models that can computerise/ automate tasks, thus reducing human efforts. On the other hand, full stack development refers to creating websites and is a combination of both front-end (client-side) and back-end (server-side).
In this article, we will discuss about ‘Machine Learning vs Full Stack Developer’. We will discuss the key differences between them. Moving forward, let's discuss about machine learning and its workflow.
Machine Learning
Machine learning is a subdomain of AI (artificial intelligence) and has been a hot topic for many years, and it is a rapidly growing field. It refers to the developing models that can automate tasks and make predictions and decisions. Machine learning requires a solid fundamental understanding of mathematics, programming languages such as Python, R, etc., and data analysis.
Machine learning is in high demand and used in various industries like finance, healthcare, etc. Machine learning involves using algorithms and datasets to develop systems that can automate tasks, thus eventually reducing human efforts. The systems are built in such a way that they can recognise patterns and make decisions and predictions according to the data.
An example of machine learning is the recommendation system used in platforms such as Netflix, amazon prime, youtube, etc., that shows us items according to our preferences by analysing the history and rating.
Machine Learning Workflow
Machine Learning workflow varies from project to project. Below are the four basic phases.
Gathering data
The quality of the data determines the accuracy of your project. So one needs to identify required sources and aggregate useful information into a single dataset.
Data pre-processing
After collecting the data, the next step involves pre-processing it. This means that the data is cleaned, verified, and formatted into a useful dataset. While aggregating many sources, one needs to ensure that the data formats match. Therefore reliability should be maintained.
Building dataset
The next step involves dividing the processed data into training, validating, and testing datasets. The training set is used in the starting step to train the algorithm and to instruct how to process the information by defining model classification.
A validation set is used for estimating the accuracy of the model and to identify any issues or mistraining in the model.
Training and refinement
This step involves proving the training set suitable algorithm in order to learn adequate features and parameters. The process also involves modifying or removing the variables. The model is refined further using the validation dataset.
Machine learning evaluation
The next step involves using a test database to verify the results of the model. The results produced by the model are compared with the actual correct answers. According to the results, the model is further improved and deployed.
Full-stack development is an evergreen field. We know that in this digital era, every business needs an online website to cater to the needs of its customers and to excel. So full, stack developers are in high demand.
A full-stack developer is one who is skilled at handling both the front-end and back-end of the website. It involves developing websites consisting of both front-end, back-end, and database. Full-stack developers can handle both aspects and are proficient in various tasks, from developing user interfaces to handling server-side logic.
Front-end Development
Front-end development refers to building the user interface (client-side) of the website. It means creating visually appealing websites and components for user interaction.
For example, you can interact with an e-commerce webpage to search for your favourite products. This is referred to as the front end of the application.
Front-end developers use technologies such as HTML, CSS, and Javascript to crete the front-end. HTML is used to give structure to the web page, CSSgives styling, and Javascript adds functionality.
HTML, CSS, and Javascript form the basis of front-end development, so as a beginner, one must excel in them and then move on to learn other frameworks and libraries.
Back-end Development
Back-end development refers to building the server side. A back-end developer is responsible for handling the server-side logic. The server can be referred to as a virtual machine that responds to the client’s request. Developers use technologies such as Node.js, Flask, and Django for developing the back end of a website.
For example, when you search for a product on an ‘e-commerce’ platform. It shows you the desired product, but what happens behind the scenes is that an HTTP request is sent from the client side to the back-end. The backend serves that request by fetching the appropriate item from the database and sending an HTTP response back to the client. Also, see Data Science
It refers to understanding database design, creating schemas for data storage,storing the data in the database, and managing it. Therefore, a full-stack developer must have a proper understanding of database management systems and should be familiar with tools like Oracle, MySQL, and MongoDB.
Machine learning is a subdomain of AI (artificial intelligence). It refers to developing models that can automate tasks and make predictions and decisions.
Full-stack development involves developing websites consisting of both front-end and back-end.
Skills
The skills needed for Machine learning include data analysis, mathematics and programming in languages such as Python, R, etc.
The skills needed for Full-stack development include HTML, CSS, Javascript (other front-end frameworks), and backend technologies like Node.js, flask, handling databases and creating APIs.
Career opportunities
Machine learning engineer, data scientist, computer vision engineer, NLP engineer, deep learning engineer, and data engineer.
Full stack developer, front-end, back-end developer, Software engineer.
Complexity
In machine learning, complex algorithms are used for building models.
The complexity in full stack development depends on the particular need of a website type.
Domain
Machine learning is generally used in data-driven decision-making.
Full-stack development is used in creating websites.
Usage
Some machine learning uses are image recognition, recommendation systems, natural language processing, sentiment analysis, fraud detection, voice recognition, etc.
Some of the uses of Full stack development include making e-commerce platforms, content management systems, financial management systems, healthcare systems, and real-time chat applications.
The machine learning model is deployed by integrating the model into a system that accepts input and processes the information.
Websites are deployed over the internet for people to access.
Debugging
Debugging is done to remove algorithmic errors. by using logging and debugging statements and by using debugging tools provided by TensorFloe, scikit-learn, etc.,
Debugging is done in the front-end and back-end code using various tools and by using logging and debugging statements.
Software Development Lifecycle
SDLC includes collecting data, processing data, building models, and evaluating, followed by deployment.
SDLC includes designing, developing, testing, deploying, and maintaining.
User experience
Providing proper predictions by improving data accuracy.
Creating responsive, interactive websites with amazing visuals.
Machine learning is a subdomain of AI (artificial intelligence) and has been a hot topic for many years, and is a rapidly growing field. It refers to the development of models that can automate tasks and make predictions and decisions.
What is Full Stack development?
Full-stack development is an evergreen field that involves developing websites consisting of both front-end and back-end. Full-stack developers can handle both aspects and are proficient in various technologies, from developing user interfaces to handling server-side logic.
What is the role of a Full stack developer?
Full-stack developers can handle both aspects and are proficient in various technologies, from developing user interfaces to handling server-side logic.
What are the uses of Machine learning?
Some machine learning uses are image recognition, recommendation systems, natural language processing, sentiment analysis, fraud detection, voice recognition, etc.
Which is more complex, Machine learning or Full stack development?
The complexity in full stack development depends on the particular need of a website type. In machine learning, complex algorithms are used for building models.
Conclusion
In this article, we have discussed ‘Machine Learning vs Full Stack Developer’. We have discussed the main differences between machine learning and web development. To know more about machine learning and full-stack development, refer to the articles below.