As Artificial Intelligence (AI) continues to progress rapidly, achieving mastery over Machine Learning (ML) is becoming increasingly important for all the players in this field. This is because both AI and ML complement each other.
While textbooks and study materials will give you all the knowledge you need to know about Machine Learning, you can never really master ML unless you invest your time in real-life practical experiments – Machine Learning projects. As you start working on machine learning projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.
Here are some cool machine learning projects that are great for beginners:
Keep reading for the links to detailed tutorials and Data Sources…
Stock Prices Predictor
Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks. And with so much data available on the stock market, it is a hotbed of opportunities for data scientists with an inclination for finance.
However, before you start off, you must have a fair share of knowledge in the following areas:
- Predictive Analysis: Leveraging various AI techniques for different data processes such as data mining, data exploration, etc. to ‘predict’ the behaviour of possible outcomes.
- Regression Analysis: Regressive analysis is a kind of predictive technique based on the interaction between a dependent (target) and independent variable/s (predictor).
- Action Analysis: In this method, all the actions carried out by the two techniques mentioned above are analyzed after which the outcome is fed into the machine learning memory.
- Statistical Modeling: It involves building a mathematical description of a real-world process and elaborating the uncertainties, if any, within that process.
Useful Data Sources:
In Michael Lewis’ Moneyball, the Oakland Athletics team transformed the face of baseball by incorporating analytical player scouting technique in their gameplan. And just like them, you too can revolutionize sports in the real world!
Since there is no dearth of data in the sports world, you can utilize this data to build fun and creative machine learning projects such as using college sports stats to predict which player would have the best career in which particular sports (talent scouting). You could also opt for enhancing team management by analyzing the strengths and weaknesses of the players in a team and classifying them accordingly.
With the amount of sports stats and data available, this is an excellent arena to hone your data exploration and visualization skills. For anyone with a flair in Python, Scikit-Learn will be the ideal choice as it includes an array of useful tools for regression analysis, classifications, data ingestion, and so on.
Useful Data Sources:
Develop A Sentiment Analyzer
Although most of us use social media platforms to convey our personal feelings and opinions for the world to see, one of the biggest challenges lies in understanding the ‘sentiments’ behind social media posts.
And this is the perfect idea for your next machine learning project!
Social media is thriving with tons of user-generated content. By creating an ML system that could analyze the sentiment behind texts, or a post, it would become so much easier for organizations to understand the consumer behaviour. This, in turn, would allow them to improve their customer service, thereby providing the scope for optimal consumer satisfaction.
You can try to mine the data from Twitter or Reddit to get started off with your sentiment analyzing machine learning project.
Useful Data Sources:
AI and ML applications have already started to penetrate the healthcare industry and are also rapidly transforming the face of global healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. They are not only helping HCPs (Health Care Providers) to deliver speedy and better healthcare services but are also reducing the dependency and workload of doctors to a significant extent.
So, why not use your skills to develop an impressive machine learning project based on healthcare?
The healthcare industry has enormous amounts of data at their disposal. By harnessing this data, you can create:
- Diagnostic care systems that can automatically scan images, X-rays, etc., and provide an accurate diagnosis of possible diseases.
- Preventative care applications that can predict the possibilities of epidemics such as flu, malaria, etc., both at the national and community level.
- Machine Learning in Health Care
- Building meaningful machine learning models for disease prediction using R
Useful Data Sources:
- Large Health Data Sets
- Indian healthcare data, information, and analytics
- WHO: Global Health Observatory (GHO) data
Prepare ML Algorithms – From Scratch!
Writing elaborate machine learning algorithms right from scratch is an excellent way to scale up your ML skills, primarily for two reasons –
- You will learn to translate mathematical instructions into programmable codes.
- You will be increasingly wired towards computational thinking.
For your machine learning project, start off with algorithms that are relatively simple and move on to the more complex ones.
- R: Logistic Regression from Scratch
- Python: Logistic Regression from Scratch
- Python: k-Nearest Neighbors from Scratch
Develop A Neural Network That Can Read Handwriting
Deep learning and neural networks are the two happening buzzwords in AI. These have given us technological marvels like driverless-cars, image recognition, and so on.
So, now’s the time to explore the arena of neural networks. Begin your neural network machine learning project with the MNIST Handwritten Digit Classification Challenge. It has a very user-friendly interface that’s ideal for beginners.
Useful Data Source:
If you wish to excel in Machine Learning, you must gather hands-on experience with such machine learning projects. Only by working with ML tools and ML algorithms can you understand how ML infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through textbooks and tutorials to build your very own machine learning projects!