According to the current situation, with AI being the focus of attention, machine learning is gaining a lot of traction. Machine learning is at the heart of the operations of companies like Google, Fb, Uber, and a slew of others. On the whole, machine learning is a popular skill in today’s market. The greater the demand for and use of this domain, the more daunting it becomes for newcomers to explore. The more projects you complete, the better at machine learning you become.
Table of Contents:
- A Machine Learning-Based Movie Recommendation System
- System for Image Cartooning
- The Iris Flower Classification Project
- A Dash scenario for visualizing and forecasting stock prices
- Machine Learning Data Preprocessing CCL
Machine learning projects to learn in 2022
1. A Machine Learning-Based Movie Recommendation System
A common and simple project, to begin with, is creating a system that suggests movies. Users will be given movie recommendations by a system that applies pertinent filters based on their preferences and browsing history. The customer attitude is observed here in relation to the data being scrolled and their ratings. This movie recommender system will be the result of a set machine learning technique being implemented.
For your recommender system, you’ll need a dataset to work with. There are numerous options available, including MovieLens, TasteDrive, and others. Preferably, choose a dataset with a large number of films and ratings. To retrieve the data, which in this case is movies and ratings, you’ll need the dataset.csv files. First and foremost, you must perform some information preprocessing in making data usable. Once you have the data, you can use Machine Learning algorithms to recommend movies and even keep track of the most popular genres in your system. Aside from a movie recommender system, you can create any other form of recommendation systems, such as a book recommender system or a café recommendation system, for example. For different recommendation systems, you can use the same procedure with the appropriate dataset.
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2. System for Image Cartooning
Why should cartoonization be left out of the use of ML Machine Learning, which is gaining traction in every field? To turn a real-life photo into an animated one, use techniques like White Box Cartoonization. When it comes to integrating Machine Learning, the basic idea behind such a system is to concentrate on expression trying to extract elements to make the process completely controllable and flexible. The white box method divides an image into three cartoon representations: Surface Representation, Structure Representation, and Textured Representation, to name a few. In addition, we use a GAN (Generative Neural Networks) structure to optimize our desired result.
You can use cartoony pictures by creating a Python prototype using OpenCV if you want a less complicated and more understandable solution. To implement Machine learning for image analysis and transformation, you’ll simply need to input ML libraries. This project would not only help you develop your skills, but it will also provide you with a self-created photo editing app. Imagenet, Tbi, ToonNet, and a slew of other online resources can provide you with a great dataset for training and testing your machine learning model. The dataset will include detailed information about a wide range of images.
3. The Iris Flower Classification Project
This is yet another popular machine learning project. The purpose of the project is to categorize iris flower species based on the length of their petals and sepals. This is a fantastic machine learning project for evaluating the life forms of a new iris flower. On the dataset of an iris flower, Machine Learning algorithms are used to draw a categorization of its species and act effectively.
The iris dataset is divided into three classes, each with 50 instances. The three types of iris, setosa, Versicolor, and verginica, are divided into these three classes. The dataset for the same is available online in CSV format. It’s also available for download from the UCI ML Repository. After you’ve prepared the data set, you’ll need to decide on a neural network to use for classification. The next step is to put the training strategy into action using machine learning algorithms. You select the best model with the best generalization ability after training your data. After you’ve found the best model, you’ll move on to the testing analysis and model deployment stages. As a result, you have your new system ready.
4. A Dash scenario for visualizing and forecasting stock prices.
You must have seen dashboards with stock price charts that are flashing to assist traders. Stockers closely monitor the stock prices of various companies’ shares in order to study trends that are developing and ensure that they do not miss out on an opportunity. By predicting the price of the stock for a specific date, you can make it much easier for traders. This project is as fascinating to work on as it appears. You can develop a web application to show company details and stock plots using Dash, a Python framework, and some Machine Learning models.
These stock plots will show the performance of a specific stock over time, depending on the stock code entered by the user. The machine learning algorithms will aid in the prediction of stock prices. To gather data and develop your dataset, you’ll need to conduct stock research. You can do so by visiting electronic trading sites such as Google Finance, StockCharts.com, Merrill, and others. For this project, you’ll need a basic understanding of Python, HTML, and CSS. Your machine learning model will calculate current stock prices and analyze pricing trends.
5. Machine Learning Data Preprocessing CCL
As you may be aware, you must process the data before feeding it to your Ml algorithm in order to convert it into an algorithm-friendly format. If you feed your model unclean data (data with missing attributes, values, or redundancy, for example), you will get unexpected results. The more crucial data preprocessing is, the more time-consuming it becomes. So, why not create your own system to pre–process your set of data for you whenever you’re ready to start a new machine learning project? This CLI tool will save you time on your other machine learning projects.
Despite this, the project is beneficial in every way. It will not only be useful for future projects, but it will also help you demonstrate your understanding of OOPs, Pandas, or exception handling. Above all, this proposal will significantly enhance the value of your resume.
It’s always beneficial to have a useful understanding of the technology you’re working on. Though school books and other learning resources would provide you with all of the information you require about any technology, you will not truly master it unless you collaborate on real-world projects. Such machine learning project ideas would then assist you in learning all of the practical aspects of your career and making you employable in the field. Such machine learning projects can be built using Python, R, or any other programming language. These are some Machine Learning project ideas with source code to assist you in learning and mastering Machine Learning. To advance your career and gain real-world experience, you must now get your hands dirty with projects.
Sai Priya Ravuri is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. She also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Machine Learning, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.