There are tons of resources available today to learn Artificial Intelligence and Machine Learning. From books, blog articles, podcasts to certification courses, beginners have easy access to the arsenal needed to equip themselves with the fundamentals, and establish a strong base in these technologies.
However, as you know theory can get you only so far. Unless you complement it with hands-on practice, there is no way you will be able to pursue a machine learning job role. The only solution here is to carry out meaningful projects in the domain. Projects are an interesting and practical way to practice how to implement machine learning to solve real world problems.
In this article, we have curated 6 machine learning project ideas that can get you started and further your learning.
Following are the six projects that would help you kickstart your career as a machine learning engineer.
Exploratory Data Analysis
Machine learning thrives on data, but before you throw the data in the mixer it’s crucial that you understand it first. Exploratory Data Analysis (EDA) deals with understanding your data and learning to transform it to better suit your needs.
For a beginner the Titanic disaster dataset is recommended to hone your EDA skills. The challenge posed is to build a predictive model to figure which passenger is likely to survive. Feature selection and transformation are core steps to cracking the problem.
Not everything’s a ‘yes’ or ‘no’ question. Questions tend to have multiple choices, and now your loss functions are a bit longer and the confusion matrix is a lot larger, and there’s no escaping it. But you could learn and the best place to start off with is the infamous Iris dataset.
Introduced by the British statistician and biologist Ronald Fisher in 1936, this dataset is still popular in the field of pattern recognition. The dataset contains three classes with fifty records of each, with four features representing the class.
A large part of online activity, be it social media, shopping or entertainment, is driven by recommender systems. With rich data of user interactions available, it wasn’t long before companies such as YouTube, Amazon and Netflix started leveraging it to bring personalised feed to their users. With the right recommendation user engagement increases and more profit follows. Therefore recommender systems are vital in establishing a successful online site.
A good dataset to start off your recommender system escapades is Netflix Price Data. This dataset was released as part of a competition hosted on kaggle.com, and contains ratings provided for 180k movies by a user base of 480k.
Beginners should look up collaborative filtering as it is a very popular technique used for such problems.
With increasing engagement in social media platforms, an often asked question is “How do people feel about X?”. This question could be of utmost importance to movie producers hiring an actor, brands looking to polish their images, and even politicians who are campaigning for votes. Sentiment analysis is a solution to all these questions.
Gauging how people feel, when the people in question range in millions and the feel in question is rather cryptic at times, is a daunting task. Proficiency in Natural Language Processing (NLP) is desired for this task.
Sentiment140 dataset is a good place to start sentiment analysis on. It contains over 1.6 million tweets which are annotated with values ranging from 0(negative) to 4(positive).
Dabbling with the twitter api to scrape tweets is also a good source of data for this project.
Automatic classification of images is a technology that has applications in fields varying from healthcare to automobile. And it’s no wonder that a large part of research in machine learning is done on improving this. Over the last decade neural networks have proved to be the best of the lot in attaining state of the art performance in this task. Understanding of neural networks,especially architectures such as convolutional neural networks, is a must for any computer vision project.
Knowledge of frameworks such as Tensorflow or Pytorch is desired.
There’s an abundance of datasets available in the public domain for image classification. One such is the Caltech 101 dataset, containing pictures of objects belonging to 101 categories.
Optical Character Recognition (OCR)
Even with the advance of the digital age, a large amount of data still remains on print or writing. In order to either create a backup or to utilize this data for other processes conversion to digital text is vital. OCR technology has assisted in solving this problem and saved many man hours by doing so.
As it is also a computer vision task, neural networks are desired for best output.
Beginners are recommended to start with the MNIST dataset. Those who are more advanced should check out Tesseract-OCR – an open source OCR.
You can never know how much you truly understand without getting hands-on. These 6 projects are an absolute must for all machine learning aspirants or even final year students who wish to establish their career in the domain.