Machine Learning (ML) is widely applied to businesses of all sizes and across disciplines. The capabilities of machine learning to compute a vast amount of data while developing intelligent algorithms allows humans to explore their creative ambitions. In the background, ML crunches data and automates mundane tasks. In short, ML allows systems to learn complex tasks and improve them without being directly programmed.
Being called the fastest-growing technologies, choosing a career in machine learning is the best decision you can make. In an industry where advancements are ever-evolving, textual knowledge would do little to help you learn the basics. Real-world machine learning projects will make you employable and enable you to succeed in your career in machine learning.
If you're starting off check out these 10 Machine Learning Models.
At GreyCampus, we believe that hands-on projects teach you how to practically approach ML in a real-world environment. Let’s explore interesting machine learning project ideas for beginners, intermediates, and advanced learners to test their knowledge.
But before we begin, it's probably better if one understands the necessity for taking up project work.
You would be familiar with the fact that in the software development industry, aspiring developers work on their personal projects. A great way to constantly build, refine, and evolve your skills is by developing real-world projects. The more you experiment with practical machine learning projects, the more knowledge you acquire.
When you start working on machine learning project ideas, you can test your skills, strengths, and weaknesses. This exposure will boost your career.
While working on machine learning projects you:
· Gain expertise in preparing a relevant problem statement
· Design a specific solution to the problem
· Gather and process the data
· Evaluate the quality of your model
· Identify your strengths and weaknesses based on which you can improvise your knowledge
In this article, we talk about the 9 interesting machine learning project ideas for beginners to get hands-on practice on ML.
The 3 machine learning project ideas are suited for those starting a career in data science or artificial intelligence. For beginners and students, these ML ideas will strengthen the base. These algorithms don’t need a deep understanding of the data science domain and can be performed by anyone who wants to learn ML.
Object detection technology is an example of powerful deep learning algorithms R-CNN, YOLO, and SSD. They have shown potential in real-time vehicle detection, assisting sports teams to analyze and build scouting reports, helping self-driving cars safely navigate through traffic, ensuring proper quality control of parts in manufacturing, and spotting violent behavior in a crowded place.
Here are 10 Machine Learning Algorithms you should know.
When working on object detection technology using deep learning algorithms you can build a model from scratch. It can go through various images present in a given scenario and classify as well as localize them precisely.
Stock prices predictor is an ML project idea that is specifically suitable for professionals looking to work in the finance domain. The idea to predict an organization’s future stock prices based on its performance and stock market’s valuations.
When starting to learn, it is advised to pick an easier machine learning project such as predicting half-yearly price movement for a company based on its quarterly report. Gradually, you can learn about different types of data such as fundamental indicators, prices, volatility indices, and global macroeconomic indicators.
Before trying your hands on this project, make sure you have basic knowledge of predictive analysis, regression analysis, action analysis, and statistical modeling. They are critical in predicting stock prices.
This machine learning project idea is based on two key pillars that are neural networks and deep learning. They paved the way for many successful projects like self-driven cars, automated text recognition, and image recognition.
You can start this project with MNIST Handwritten Digit Classification Challenge which is a beginner-friendly interface idea for Java professionals.
Although this idea challenges your knowledge about data science, it doesn’t need high-level computational skills. You will learn how to build a neural network from scratch that can accurately solve the MNIST challenge.
The ML projects for intermediate professionals are somewhat advanced and require basic expertise of the field. Working on one of these projects can advance your career by learning from the best real-world examples.
The cashless economy has made the banking sector most vulnerable to credit card frauds and identity thefts. A major concern for both customers and banks, fraudsters have siphoned off billions of dollars from the banking scams.
This ML project aims to develop a fraud detection system that helps in finding anomalies in the workflow and prevent swindling of funds.
Source: Data Science Central
Working on imbalanced data can be a challenge for those looking to work on such a project. A case study published on towards data science found that out of 284,807 transactions, 492 were fraudulent. They focused on three key strategies: oversampling, under-sampling, and a combined approach. The combined approach helped them in achieving great precision when dealing with imbalanced datasets.
This is again an imbalanced data analysis machine learning project which aims at developing a predictive model to determine the income levels of a particular set of people in a region. The data sets used are age group, demographics, education, marital status, and occupation.
For this machine learning project for intermediates, the four key steps involved are:
An interesting machine learning project idea for intermediates, social media sentiment analysis is a real-time problem. You can use the data from Reddit or Twitter to understand the sentiment behind the social media posts we keep sharing regularly.
Social media platforms have heaps of user-generated content. Building an ML project that can analyze that content and understand the sentiment behind has numerous applications. The sentiment analysis can help brands to gain insights into customer behavior. Based on the sentiment, brand perception and customer service can be improved.
Advanced machine learning projects will supercharge your career in data science, machine learning, and artificial intelligence domains. Here are the 3 advanced applications of ML already (and commonly) in use.
Personalization is the key pillars of Netflix. Right from the suggested videos (which are different for every user) to their ranking, the artwork, and the way they are organized in rows and pages. Each experience is personalized.
They use machine learning to drive large scale personalization and search experiences to millions of Netflix users. This algorithm extends to A/B testing and offline experiments.
Working on such a high-level ML project might be quite challenging but it will give you a lifetime experience expertise to deal with complex projects.
You can use deep learning frameworks such as Keras and PIL, jupyterlab, pandas, and scikit-learn to classify traffic signals available into different categories.
The ability of the algorithm to understand traffic signals is crucial for autonomous vehicles.
This Python-based project aims at developing models capable of generating captions of images in natural language such as English with the help of computer vision and natural language processing technologies by understanding their context.
Source: John Pace
For working on this ML project idea, you have to leverage two techniques of deep learning together: Convolutional Neural Networks and a type of Recurrent Neural Network (LSTM)
Accelerate your career as a data scientist by implementing machine learning project ideas. Pick one based on your knowledge and interest and start building your machine learning algorithms.
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