8 AI/Machine Learning Projects To Make Your Portfolio Stand Out
If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.
By Kajal Yadav, a freelance writer on data science, startups & entrepreneurship.
Source Unsplash, edited by the author.
Are you excited to enter the Data Science world? Congrats! That’s still the right choice because of the ultimate boost in need of work done in Data Science and Artificial Intelligence during this pandemic.
Although, because of the crisis, the market currently gets tougher to be able to set it up again with more men force as they are doing earlier. So, It might possible that you have to prepare yourself mentally for the long run hiring journey and many rejections in along the way.
Hereby, while writing this article, I am assuming that you already know that a data science portfolio is crucial and how to build it up.
You might spend most of your time doing data crunching and wrangling and not applying fancy models.
One question that I have asked on and on by data science enthusiasts is that what kind of projects they should include in their portfolio to build a tremendously good and unique portfolio.
Below I give 8 unique ideas for your data science portfolio with attached reference articles from where you will get the insights of how to get started with any particular idea.
1. Sentiment analysis for depression based on social media posts
This topic is so sensitive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main cause of disability worldwide and is a significant supporter of the overall global burden of disease, and nearly 800,000 individuals consistently bite the dust because of suicide every year. Suicide is the second driving reason for death in 15–29-year-olds. Treatment for depression is often delayed, imprecise, or missed entirely.
Internet-based life gives the main edge chance to change early melancholy mediation services, especially in youthful grown-ups. Consistently, roughly 6,000 Tweets are tweeted on Twitter, which relates to more than 350,000 tweets sent for each moment, 500 million tweets for every day, and around 200 billion tweets for each year.
As indicated by the Pew Research Center, 72% of the public uses some sort of internet-based life. Datasets released from social networks are important to numerous fields, for example, human science and brain research. But the supports from a specialized point of view are a long way from enough, and explicit methodologies are desperately out of luck.
By analyzing linguistic markers in social media posts, it’s possible to create a deep learning model that can give an individual insight into his or her mental health far earlier than traditional approaches.
- You Are What You Tweet – Detecting Depression in Social Media via Twitter Usage
- Early Detection of Depression: Social Network Analysis and Random Forest Techniques – Original paper, University of A Coruna.
- Depression detection from social network data using machine learning techniques
2. Sports match video to text summarization using neural network
So this project idea is basically based on getting a precise summary out of sports match videos. There are sports websites that tell about highlights of the match. Various models have been proposed for the task of extractive text summarization, but neural networks do the best job. As a rule, summarization alludes to introducing information in a brief structure, concentrating on parts that convey facts and information, while safeguarding the importance.
Automatically creating an outline of a game video gives rise to the challenge of distinguishing fascinating minutes or highlights of a game.
So, one can achieve that using some deep learning techniques like 3D-CNN (three-dimensional convolutional networks), RNN (Recurrent neural network), LSTM (Long short term memory networks), and also through machine learning algorithms by dividing the video into different sections and then applying SVM (Support vector machines), NN (Neural Networks), and k-means algorithms.
For better understanding, do refer to the attached article in detail.
- Scene Classification for Sports Video Summarization Using Transfer Learning – This paper proposes a novel method for sports video scene classification.
3. Handwritten equation solver using CNN
Among all the issues, handwritten mathematical expression recognition is one of the confounding issues in the region of computer vision research. You can train a handwritten equation solver by handwritten digits and mathematical symbols using Convolutional Neural Network (CNN) with some image processing techniques. Developing such a system requires training our machines with data, making it proficient at learning and making the required prediction.
Do refer to the below-attached articles for better understanding.
- Handwritten Equation Solver using Convolutional Neural Network
- vipul79321/Handwritten-Equation-Solver – An Handwritten Equation solver using CNN Equation can contain any digit from 0-9 and symbol.
- Computer Vision — Auto grading Handwritten Mathematical Answer sheets – Digitizing the steps of solving a mathematical equation written by freehand on a paper.
- Handwritten equations to LaTeX
4. Business meeting summary generation using NLP
Ever got stuck in a situation where everyone wants to see a summary and not the full report? Well, I faced it during my school and college days, where we spent a lot of time preparing a whole report, but the teacher only has time to read the summary.
Summarization has risen as an inexorably helpful way to tackle the issue of data over-burden. Extracting information from conversations can be of very good commercial and educational value. This can be done by feature capture of the statistical, linguistic, and sentimental aspects with the dialogue structure of the conversation.
Manually changing the report to a summed up form is too time taking, isn’t that so? But one can rely on Natural Language Processing (NLP) techniques to achieve that.
Text summarization using deep learning can understand the context of the entire text. Isn’t it a dream come true for all of us who need to come up with a quick summary of a document!
Do refer to the below-attached articles for better understanding.
- Comprehensive Guide to Text Summarization using Deep Learning in Python – “I don’t want a full report, just give me a summary of the results.”
- Understand Text Summarization and create your own summarizer in python – Summarization can be defined as a task of producing a concise and fluent summary while preserving key information.
5. Facial recognition to detect mood and suggest songs accordingly
The human face is an important part of an individual’s body, and it particularly plays a significant role in knowing a person’s state of mind. This eliminates the dreary and tedious task of manually isolating or grouping songs into various records and helps in generating an appropriate playlist based on an individual’s emotional features.
People tend to listen to music based on their mood and interests. One can create an application to suggest songs for users based on their mood by capturing facial expressions.
Computer vision is an interdisciplinary field that helps convey a high-level understanding of digital images or videos to computers. Computer vision components can be used to determine the user’s emotion through facial expressions.
There are these APIs, too, that I found interesting and useful. However, I didn’t work on these but attaching here with a hope that these will help you.
- 20+ Emotion Recognition APIs That Will Leave You Impressed, and Concerned | Nordic APIs – If businesses could sense emotion using tech at all times, they could capitalize on it to sell to the consumer.
6. Finding out habitable exo-planet from images captured by space vehicles like Kepler
In the most recent decade, over a million stars were monitored to identify transiting planets. Manual interpretation of potential exoplanet candidates is labor-intensive and subject to human mistake, the consequences of which are hard to evaluate. Convolutional neural networks are fit for identifying Earth-like exoplanets in noisy time-series data with more prominent precision than a least-squares strategy.
- Exoplanet hunting using Machine Learning – Hunting worlds beyond our solar system.
- Artificial Intelligence, NASA Data Used to Discover Exoplanet – Our solar system now is tied for most number of planets around a single star.
7. Image regeneration for old damaged reel picture
I know how time-consuming and painful it is to get back your old damaged photo in the original form as it was earlier. So, this can be done using deep learning by finding all the image defects (fractures, scuffs, holes), and using inpainting algorithms, so that one can easily discover the defects based on the pixel values around them to restore and colorize the old photos.
- Colorizing and Restoring Old Images with Deep Learning – Colorizing black and white images with deep learning has become an impressive showcase for the real-world application.
- Guide to Image Inpainting: Using machine learning to edit and correct defects in photos
- How To Perform Image Restoration Absolutely DataSet Free
8. Music generation using deep learning
Music is an assortment of tones of various frequencies. So, automatic music generation is a process of composing a short piece of music with the least human mediation. Recently, deep learning engineering has become the cutting edge for programmed music generation.
- Music generation using Deep Learning
- How to Generate Music using a LSTM Neural Network in Keras – An introduction to creating music using LSTM Neural Networks
I know that it’s a real struggle to build up a cool data science portfolio. But with such a collection that I have provided above, you can make above-average progress in that field. The collection is new, which gives the opportunity for research purposes too. So, researchers in Data Science can also choose these ideas to work on so that their research would be a great help for Data Scientists to start with the project. Moreover, it’s fun to explore the sides that nobody has done before. Although, this collection actually constitutes ideas from beginning to advanced levels.
So, I will not only recommend this for newbies in the data science area but also senior data scientists. It will open many new paths during your career, not only because of the projects but also through the newly gained network.
These ideas show you the broad range of possibilities and give you the ideas to think out of the box.
For me and my friends, the learning factors, adding value to the society, and the unexplored knowledge is important, and the fun in a way is essential. So, basically, I enjoy doing such projects that give us a way to gain immense knowledge in a way and let us explore the unexplored dimensions. That is our main focus when dedicating time to such projects.
Original. Reposted with permission.
Bio: Kajal Yadav is a freelance writer specializing in data science, startups, and entrepreneurship. She writes for several publications and at the same time works with startups on their content marketing strategies.
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