Artificial intelligence and machine learning have significantly facilitated our lives with the valuable advantages they offer.

Many fields, such as data science, with the vast amount of data they need to analyze, utilize machine learning to streamline the entire process.

For those professionally involved or taking their first steps in data science, machine learning is a fundamental pillar and a preferred choice for developing and creating projects, enhancing their portfolio.

Therefore, in today's article, we will explore:

 Where machine learning is used

 5 machine learning projects to start as a beginner

Let's start by explaining some basic things about machine learning.

 Where is Machine Learning Used?

As we have seen in our previous articles, machine learning is a subfield of artificial intelligence (AI) that is based on the idea that computers learn from the data they collect and, through analysis, can recognize patterns and make predictions about the future.

Machine learning is used in a wide range of applications and by many well-known companies.

For example, Facebook uses machine learning to personalize the display of each member's news feed.

Additionally, smart assistants typically combine supervised and unsupervised machine learning models for natural language processing (NLP).

Furthermore, CRM software can use machine learning models to analyze various emails, allowing sales and marketing teams to respond first to the most important messages.

Now, let's take a look at some machine learning projects that you can start if you are a beginner and want to build your knowledge.

 5 Machine Learning Projects to Start as a Beginner

Here are some interesting machine learning projects that you can start as a beginner:

   Project #1: Human Activity Recognition with Smartphones

Many mobile devices have been designed to automatically detect when we engage in an activity, such as running or cycling.

To practice this type of project, you can use a dataset containing records of physical activity states collected through smartphones equipped with inertial sensors.

Then, you can create classification models that accurately predict future activities.

   Project #2: Stock Price Predictions

Stock price predictions are based on datasets containing previous prices and volatility indicators. If you're a beginner, you can start with such a project and use stock market datasets to create predictions for the upcoming months.

This particular project is a good way to familiarize yourself with creating predictions based on large datasets (Big Data).

   Project #3: Handwritten Character Recognition

One of the challenging tasks for software applications has been understanding the text contained in an image, especially when the image includes handwritten text.

This is where machine learning comes in, making this complex problem relatively easy to solve.

Here's how it works:

In machine learning, all you need is access to a well-labeled dataset that includes handwritten characters and corresponding labels indicating what is written.

Then, you can use machine learning algorithms to train a model that can be used in the future for making predictions.

This machine learning project can make good use of different deep learning techniques and neural networks.

The accuracy of the model depends significantly on the algorithms used, as well as the dataset.

For image learning, you can use a Convolutional Neural Network (CNN), a type of neural network that can be constructed and trained using frameworks like TensorFlow and Keras, among others.

   Project #4: Sign Language Recognition System

Numerous tools are being developed to make the lives of people with disabilities a little easier.

As many individuals who are unable to speak use sign language to communicate with those around them, a tool that can assist them, especially in terms of improving accessibility, is a sign language recognition system.

In this system, you can use computer vision to analyze and detect user gestures, as well as provide commands to a system or application.

This system can be used to provide voice assistants for individuals who cannot speak and can also be trained with sign language vocabulary, allowing these individuals to convert their sign language into text or sound for others to interpret and understand.

   Project #5: Sports Match Outcome Prediction

As mentioned earlier, machine learning algorithms thrive on massive amounts of data from which they learn and make predictions.

In football, for example, data used frequently by sports analysts includes team and player performance metrics, such as the number of goals scored by each player, as well as the results of previous matches between teams.

In this machine learning project for sports predictions, creating a classification model based on a training dataset is crucial.

There are two training methods: supervised and unsupervised.

The supervised method involves creating prediction models based on both input and output data, while the unsupervised method only involves input data.

Of course, the quality of the data and the selection of the right dataset are essential parts of sports outcome prediction using machine learning.

The use of artificial neural networks (ANN) allows for the inclusion of even more factors, leading to more accurate predictions.

 In Summary

To sum up, we discussed 5 machine learning projects for beginners to gain more experience and upgrade their portfolio.

If you are excited and want to learn more about data science and machine learning, follow us for more educational articles!