Machine learning is a simple study of teaching a computer program or algorithm that enables one to gradually improve upon a set task provided at a high level. Several machine learning applications are already popular, such as image recognition, fraud detection, recommendation systems, and more. Machine Learning tasks make human tasks effortless and make them efficient and automatic, saving time and delivering a high-end product.
Even Google, the most popularly used search engine has machine learning integrated at its core. From understanding the user's query and adjusting the result based on the results to displaying the trending topics and advertisements in relation with the query. The future is not far when the technology would be insightful and self-correcting.
Imparting knowledge about Machine Learning amongst your colleagues and integrating them into your projects and applications would prove beneficial for both your career and your company.
Machine Learning Projects
So go ahead and try out the challenging and exciting machine learning projects that we have mentioned and gear up to be praised in your group.
1. Stock Price Prediction
The project is about predicting future stock returns based on the past returns and numerical news indicators by examining different forecasting techniques to construct a portfolio of multiple stocks to diversify the risk.Supervised learning methods are used for stock price forecasting by interpreting chaotic market data.
Checkout the Source Code here.
2. Wine Quality Testing
Wine is one of the exotic alcohol drinks that takes years of fermentation to produce. Hence, the ancient bottle of wine is an expensive and high-quality wine it is. Choosing a perfect bottle of wine needs several years of experience in wine tasting and yet it could be a hit or miss affair. The wine quality test project compares wines based on their physicochemical tests like alcohol content, fixed acidity, density, pH, and more. The project also calculates the quality standards and proportions of the wine. Thus making shopping for wine easy.
Checkout the Source Code here.
3. Sentiment Analysis
Human emotions are generally categorized as negative, positive or neutral and the process of analyzing the human emotions is termed as sentiment analysis. A sentiment analyzer learns about various sentiments post of the user on social media. Platforms like Facebook, Reddit, YouTube, Twitter generate massive amount of big data that is mined to understand trends, public sentiments and opinions through Machine Learning and Artificial Intelligence. The project would help organizations to understand consumer behaviour thus improving their customer services and thus providing optimal customer satisfaction.
Checkout the Source Code here.
4. Handwritten Character Recognition
When we learn about AI, the most exciting and consumer term that pops up is “Neural Network” which in simple words training a system like training a child’s brain by making the system familiar to patterns and then testing it repeatedly.
The handwritten character recognition project requires modeling of neural networks to detect and recognize handwritten characters like alphabets and number. It is one of the challenging projects but does not require much computation.
Checkout the Source Code here.
5. Iris Flower Classification
Iris flower classification project is popular among beginners and is considered as a best idea to start experimenting with ML projects. Iris flowers are one of the varied species and are distinguished based on the length of sepals and petals.
The goal of this project is to classify the flowers into three species: virginia, setosa, versicolor. The project uses Iris flower dataset for classification tasks helping learners to figure out the basics of handling numeric value and data. Iris flower dataset is a small dataset and easily fits in memory without requiring scaling capabilities.
Checkout the Source Code here.
6. Writing Machine Learning Algorithms from Scratch
Writing ML algorithms from scratch is the simplest and the best way to get your hands on Machine Learning and understand the concepts in detail. This project would allow you to learn to transform mathematical instructions into functional code. You may want to begin with by choosing an algorithm that is easy and straightforward and then eventually work your way up to complex ones simple linear regression, polynomial regression, logistic regression are some of the algorithms that you must try. You might also get your hands on popular algorithms like k-NN, k-Means Clustering, Naive Bayes, Apriori, and more. So get started with this project idea and improve your Machine Learning skills.
Checkout the Source Code here.
7. Barbie with Brains
Barbies have always been attractive toys for toddlers and young children. With an advancement in technology and the concept of AI and ML being incorporated in almost everything it would be exciting to have people that can speak and interact. Therefore, through NLP and some concepts of voice assistants Barbie can be trained to interact with children in logical conversation.
The doll records the user's dialogue with the help of the microphone on her necklace. The dialogues are then analyzed at the Toytalk servers which have around 8,000 lines of dialogues available for interaction.
Checkout the Source Code here.
8. Movie Recommendation with MovieLens Dataset
Recommendation system concepts have made its mark in every industry be it online shopping or grocery, movie streaming or music streaming the concept is high in demand to target its valuable customers. All popular platforms like Netflix, Amazon Prime Video, Hulu, AppleTv have efficient recommender systems for their target audience. This project uses MovieLens dataset and can be coded in Python or R. It proves to be an excellent project for the beginners to add to the portfolio.
Checkout the Source Code here.
9. Healthcare Application
Healthcare industry is also giving tough competition to other industries when it comes to acquiring Machine Learning projects. Introducing Machine Learning in healthcare could automate things thus taking alternative workload of the medical staff. With the help of data and previous case studies we can create systems or applications such as:
- Diagnostic Care System: A system that can be trained to automatically scan images, x-rays, etc and provide an accurate diagnosis of possible disease.
- Preventive Care Applications: A system trained to predict the possibility of epidemic being spread at national or community level.
- Insurance: Designing insurance premiums based on publicly available risk factors.
Checkout the Source Code here.
10. Uber Data Analysis
The popular ride sharing app is not behind when it comes to using and integrating ML and deep learning in their applications. It handles billions of rides every year helping commutters to travel at any hour. Because it has a vast reach to its customers it requires excellent customer service support to solve customer issues at the earliest.
The project aims to improve the effectiveness of customer support with deep learning techniques. Uber has a dataset of millions of pick up to analyze the customer rides and visualize to find insights and further improve customer experience.
Checkout the Source Code here.
Machine Learning Intro for Python Developers
Conclusion
To conclude, we have mentioned some of the popular ML projects that would help you take a test of Machine Learning programming and also get hold of its concepts and implementation. Knowing to integrate Machine Learning is a plus in your career as the technology is taking over in every field. We suggest you try your concepts and code all your algorithms while you learn Machine Learning. Writing algorithms while you learn this is even bigger than doing a project and also gives you an edge for knowing the concepts thoroughly.
You may also want to check out machine learning courses and books if you want to learn more in depth. Once you are confident enough you may want to appear for an interview or may be just check your skills visit machine learning interview questions to test your knowledge.
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