Machine learning offers an abundance of data to learn from and run models. Everybody has been talking about its applications and use cases. The machine learning market has grown manifold in the last couple of years. According to Fortune Business Insights 2020, the global ML market is expected to reach a value of $117.19 billion by 2027. In fact, as of April this year, more than 98,000 jobs listed on LinkedIn had ML as a required skill. It is safe to say that the popularity of ML is on the rise, and the demand for its knowledge is only going to get bigger.
If you are planning to start a career or even shift to this lucrative industry, the best way is to get hands-on experience and develop a project. Here is a curated list of top machine learning projects to help you kick start your ML journey.
Movie Recommendations
With almost everyone sticking to their television or mobile phone screens to find out the next show to binge-watch or movie to wind up the day, it is interesting to notice how our OTT profiles recommend the right content. These recommendations are based on individual viewers’ history and preferences and are made using ML algorithms.
Figuring out the next movie recommendation is a fun project for beginners who can code in Python or R, depending on their programming language preferences.
Dataset available: TMDB 5000 Movie Dataset.
Wine Quality Predictions
Wine is differentiated based on its flavour, undernotes, smell and colour. The taste of wine is acquired over time, so how do we know if one is good or not in the absence of a wine expert? Machine learning to the rescue. Beginners can use the R programming language to build an ML model to predict the quality of wine with the help of available datasets, using data exploration, regression models and data visualisation.
Dataset available: UCI Machine Learning Repository’s Wine Quality Dataset– this dataset has input variables including the pH of the wine, citric acid and residual sugar levels, density, volatile acidity, among others.
Sorting Tweets on Twitter
This beginner-level ML project allows programmers to create an algorithm that can scrape tweets using a natural language processor. The algorithm can determine tweets matching specific themes, keywords, or individuals. Using a sentiment analysis with a text mining algorithm will thus allow one to filter tweets quickly and seamlessly.
Dataset available: Sentiment140 dataset, which contains 1,600,000 tweets extracted from the Twitter API to detect sentiment.
Click here to check the top open-source NLP projects on GitHub this year.
Converting Handwritten Documents into Digital Files
An easy and fun project for beginners starting out with machine learning would be building an algorithm to convert handwritten documents into digital notes or files. Also known as the offline Handwritten Text Recognition (HTR) system, this algorithm can transcribe the text in scanned images into digital text. To build this algorithm, one would need Python, TensorFlow, NumPy and OpenCV. Building this algorithm would help beginners try their hands in concepts like image recognition, deep learning and neural networks. The project uses logistic regression. Real-life use cases include converting physical medical prescriptions into digital records or converting physical copies of documents required for availing insurance services to digital documents.
Dataset available: The MNIST Database of handwritten digits has a training set of 60,000 examples and a test set of 10,000 examples.
To know more about TensorFlow-based projects for beginners, click here.
Loan Prediction
Loans form an integral part of any bank. It is the loan interest from which banks earn most of their profits. Beginners can use Python to build simple ML projects for loan prediction. The project will introduce developers to concepts like logistic regression, decision tree and random forest.
Dataset available: Loan prediction dataset ML project.
Fake News Detection
The fake news detection model will introduce programmers to the concept of text classification. This hands-on project trains the deep learning model to detect fake news from a given news compilation. Beginners can use Python for this project.
Dataset available: Fake News.
To know about how ML projects helped fight fake news fatigue during the COVID-19 pandemic, click here.
