If you are a newcomer in the data science, artificial intelligence, and machine learning industry, you may feel overwhelmed by the topics or subjects. After going through one or two courses on the same subject, things may seem difficult for you to proceed further.
Although you may have understood the theoretical concepts related to machine learning and artificial intelligence, you probably don’t have any knowledge or experience with the tools and frameworks of machine learning and deep learning.
However, if you work on different machine learning projects, will you be able to enhance your knowledge and overall skill set and get ahead of your peers regarding job opportunities? Therefore, this article will discuss different types of machine learning projects for beginners and how you can work on them.
Deep Learning Projects: Why is it Important?
Due to massive technological developments in the recent few years, artificial intelligence and deep learning projects have been widely adopted. Moreover, technological development has successfully boosted computing capacity, which is why processing data has become easier and faster.
There have been incredible developments in the application of deep learning, including object detection applications, self-driving cars, and facial recognition software, among others. In addition, machine learning and artificial intelligence have impacted other industries, such as logistics, manufacturing, agriculture, transportation, retail, etc.
Due to the massive implementation of artificial intelligence and deep learning technology, there has been a sharp surge in demand for deep learning engineers. Big tech companies such as Microsoft, IBM, Nvidia, Meta, etc., are hiring deep learning engineers to work on their projects. Henceforth, mastery over artificial intelligence and machine learning technology will give you an edge in finding a proper job in this competitive market.
How to Start A Deep Learning Project?
As a newbie, if you want to build a machine learning or deep learning project, then first of all, you will have to identify certain problems. Then you will have to understand how you can use artificial intelligence to solve that particular problem. There are lots of open-source datasets readily available.
You can easily download them from specific platforms and then prepare them for your machine-learning project.
When preparing your data, ensure you have discarded all the irrelevant information and features from them. You may also have to modify your data if it is unstructured. Use exploratory data analysis (EDA) to reveal hidden links and correlations in your data. Then, when your data is fully ready, you can use them to build your machine learning project.
Different Kinds of Deep Learning Projects for Beginners
In this section, we will discuss some amazing deep learning projects for beginners, and you can certainly use these projects to create one for yourself. Also, note that these machine learning projects are very easy to understand and implement. Therefore, you can easily use them to enhance your machine-learning knowledge and skills.
Machine Learning Project Based on Music Recommendation System
The music recommendation system is one of the most popular machine learning projects for beginners. The recommendation system is something that suggests things you will like, or you would want to buy (in the case of e-commerce websites). You may have already encountered this system while using a shopping website, movie streaming platform, popular music streaming platform, etc.
Machine learning project concept: If you want to build a machine learning model for music recommendation, you need to have a dataset of a music streaming service. Based on the already-liked choices of a listener, this system would recommend songs released by new artists.
To successfully build this project, you need to predict the likelihood of a user repeatedly listening to a certain kind of song or music within a specified time. In this dataset, you will find information about users who have repeatedly listened to different types of songs. You can build this recommendation system with neural networks or classification machine learning algorithms.
Prediction of Home Value - Machine Learning Project
If you are considering moving to a new city for some reason, then it is essential to look for a place (typically a house or flat) to stay in. In this case, you may encounter a situation where you don’t know what to do and don’t have any sources to rely on. The same situation will apply if you are interested in buying or selling a house or any other real estate property.
When a group from the tech giant Microsoft faced the same situation, they tried to think of a solution. Their solution was to create a reliable platform where anyone can access real and credible information about the place they want to purchase or move into. But, more importantly, this specific platform should be accessed online. The platform created as a result was Zillow.
In 2006, they created a platform named Zillow where people can easily get information on different properties and real estate.
Similarly, this platform (which is also a company right now) has introduced a feature named Zestimate using which, people can know about the overall value of any property depending on multiple factors such as its history, previous sales data, available public data, etc. Currently, Zestimate possesses information about 97 million properties.
So, you can ask how Zillow and Zestimate are related to machine learning? First, we must understand how Zestimate works or functions to answer this question. For starters, Zestimate evaluates the worth of your entire house or property by collecting appropriate data and information about the same thrice a week.
This means if you make any changes to your property (for example, renovate it), it will automatically be reflected. It will also decide if the value of your property has increased, decreased, or remained the same based on the publicly available data and sales information.
Machine learning project concept: With the help of machine learning, you can make a similar model with which you can predict the value of a certain property or house. In this scenario, you can use the Zillows Economics dataset while creating your price prediction model for any property.
You should also include XGBoost-based components such as the total number of hospitals near the property, the number of schools located near the property, the crime rate, the number of parks, the average income of the people living nearby, etc. Through this project, you can answer questions such as high rent value states, least rented areas in a state, which area is appropriate for purchasing a new property or house, etc.
Predicting Stock Prices with TimeSeries
If you are interested in the financial industry, you should try creating a machine learning project to predict stock prices. The primary goal of this system would be to monitor a company's operations and anticipate its stock prices.
When you choose to work with share price data, various challenges are associated with you. For starters, you will have to deal with multiple data types, including fundamental indicators, volatility indices, market conditions, international macroeconomic indicators, etc. Not to mention all of this information is detail-oriented, meaning you have to handle a large amount of data.
You should start small if you are willing to make a machine learning model on the share price. For example, you should use fundamental indicators of any company and use them to predict that company's share price for three months. The fundamental indicator for the company can be found in the quarterly report published by the organization. Stock market datasets are also available on the internet, and you must use time series forecasting methods to execute this project.
Machine learning project concept: In machine learning, time series is a concept that evaluates events that occur at regular intervals. You will use this time series concept to predict a company’s stock price. Although multiple time series models are available, you should use one that matches the variables you will be working with.
Classifying Different Handwritings - Machine Learning Project
Earlier, we mentioned how machine learning and artificial intelligence had made an impact in different fields, which includes the likes of self-driving cars, facial recognition, etc. It can also be used for automatic text generation, image processing and recognition. Using this principle, we can create a machine learning model that will classify different types of handwriting.
Machine learning project concept: To build this machine learning model, you must acquire an easily controllable dataset such as the MNIST dataset. As a beginner, you would aim to pick up something that can be resolved without too much complexity, and the MNIST dataset can aid you in this regard. However, although the MNIST dataset is user-friendly for beginners, you will find enough challenges while dealing with handwritten digit recognition.
Sales Prediction - Machine Learning Project
Suppose you are a novice who wants to build your skill in artificial intelligence and machine learning. In that case, you need to work on multiple projects, including the ones with unsupervised machine learning algorithms. For example, one such machine learning project for beginners includes using the dataset of a supermarket to predict the current sales and what you need to do to achieve more sales.
Machine learning project concept: You can easily get your hands on the BigMart sales dataset. It is a dataset that consists of sales data from 10 different cities, and the total number of products included in the dataset is 1559.
Using this, you will try to build a machine learning model to provide information on futures sales of the 1559 products in 10 different outlets. But, more importantly, using this machine learning model, you will be able to comprehend the characteristics of the products and in which places you will be able to further raise overall sales.
Stock Price Predictions Using Artificial Intelligence and other Deep Learning Projects
Suppose you want to gain experience in artificial intelligence and machine learning projects by yourself. In that case, you have to initiate your journey from somewhere. The machine mentioned above learning projects (especially) stock price predictions can be a good starting point for your machine learning journey.
Most of these projects are rather simple to commence, and after you have become well-versed, you can start working on more complex projects to enhance your skill sets and gain more experience. However, you need to remember that before starting to work on them, you will have to become well-versed in the fundamentals of Python, Keras, etc.
More importantly, you also need to remember that constructing a deep learning or machine learning project from scratch is extremely time-consuming. While you are working on them, you have to simultaneously develop multiple skills which are required for executing them.
Keep in mind that if you work on these machine learning projects, then you will be able to easily develop the skills needed to work as a deep learning engineer. While you apply for a job in a company, you can certainly Include examples of these projects on your resume to make yourself stand out to recruiters. As a result, you can successfully receive a job in the machine learning and data sector.
While working on these machine learning projects, if you get stuck somewhere and need external help, then you should visit Superprof. It is an online platform where you can contact the best machine learning and deep learning tutors who can help you with these projects. More importantly, if you are looking for an instructor who lives in your locality or near you, you can also reach out to them through the platform itself.
Trending Machine Learning Projects For Beginners
Machine learning projects are projects in which a machine learning model is developed, trained, and deployed for a specific task or set of tasks.
There are several machine learning projects that are well-suited for beginners, including:
- Handwritten digit recognition using the MNIST dataset: This project involves training a model to recognize handwritten digits using the MNIST dataset, which contains over 60,000 images of handwritten digits.
- Image classification: This project involves training a model to classify images into different categories, such as animals, objects, or scenes.
- Sentiment analysis: This project involves training a model to classify text as positive, negative, or neutral sentiment. Computers can interpret data text the same way humans can. This would ideally help in CRM-related jobs!
- Linear regression: This is a basic project that involves predicting a continuous value (like price) from input features (like square feet) using a linear model.
These projects can be done using popular open-source machine learning libraries such as TensorFlow, Scikit-learn, and Keras.
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