A list of top machine learning projects to learn machine learning
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Machine learning is exactly what it sounds like: the concept that various types of technology, such as tablets and computers, can learn something from programming and other data. It appears to be a futuristic notion, however, most people utilize this level of technology every day. An outstanding example is speech recognition. Siri and Alexa, for example, employ the technology to repeat reminders, answer inquiries, and carry out requests. As machine learning becomes increasingly prevalent, an increasing number of people are pursuing employment as machine learning engineers.
While theoretical machine learning expertise is vital, hiring managers place a premium on production engineering abilities when filling a machine learning post. Aspiring machine learning engineers must develop applicable skills through project-based learning in order to be job-ready. Machine learning projects may be utilized to reinforce various technical topics and to demonstrate a dynamic skill set as part of your professional portfolio. You will be able to uncover machine learning project ideas that inspire and challenge you regardless of your skill level.
Machine Learning is currently one of the most prominent new technologies. And executing projects is the greatest way to learn this technology. Other methods, like online classes and reading books, can only help with grasping the fundamentals of ML; nevertheless, it is only through working on projects with real-world data that one can properly study the field. This post contains 100 Machine Learning Projects that you may undertake to learn more about machine learning technology than you ever have before!
1. Age and Gender Detection with Python
Facial analysis from photos has sparked a lot of attention since it can help with a variety of issues such as better consumer ad targeting, better content recommendation systems, security monitoring, and other disciplines. Age and gender are significant aspects of face traits, and determining them is the foundation of facial analysis and a necessary step for such jobs. Many businesses use these technologies for a variety of goals, including making it simpler to engage with clients, better adapt to their requirements, and providing a positive experience for them. People’s requirements are simpler to recognize and forecast depending on their gender and age.
2. Amazon Alexa Reviews Sentiment Analysis
Amazon Alexa is an Amazon cloud-based speech service that allows users to connect with technology. With over 40 million Alexa users worldwide, assessing user emotions regarding Alexa will be an interesting data science topic. So, if you want to understand how to assess user attitudes using Amazon Alexa, this is the post for you. Amazon Alexa’s machine learning project Reviews Sentiment Analysis Python can be a nice choice.
3. Amazon Product Reviews Sentiment Analysis
Amazon is a global firm based in the United States that specializes in e-commerce, cloud computing, digital streaming, and artificial intelligence goods. However, it is most recognized for its e-commerce platform, which is one of the largest online shopping platforms in the world today. Customers buy so many things from Amazon that the company now earns an average of $ 638.1 million each day. So, with such a vast client base, analyzing the feelings of Amazon product reviews will be a fantastic data science endeavor. As a result, the Amazon Product Reviews Sentiment Analysis Python project may be the ideal alternative for you.
4. Amazon Recommendation System
Recommendation Systems are one of the most extensively utilized Data Science applications in most firms that sell products and provide online services. Amazon is a prime example of such a company. Amazon, as an online purchasing company, must produce individualized suggestions in order to deliver a better customer experience. Amazon’s Recommendation System is built on the premise of creating product-based recommendations, which implies that you may evaluate the similarities between two items and propose the most comparable products to the customer. Researchers have long been interested in approaches for assessing similarities between two items.
5. Autocorrect Keyboard with Python and Machine Learning
Almost every smartphone maker, regardless of budget, has an autocorrect option on their keyboards. Natural language processing underpins autocorrect in the context of machine learning. It is programmed to fix typos and mistakes while typing, as the name implies. The Autocorrect model is set up to correct typos and mistakes as you type and choose the most relevant and related terms. It is entirely based on NLP, which matches words in the vocabulary dictionary to words entered on the keyboard. If the entered word is in the dictionary, autocorrect assumes you typed the correct phrase. If the term does not exist, the program finds the closest words in the smartphone’s history.
6. Automatic License Number Plate Recognition System
The goal of this project is to recognize license plate numbers. You will use OpenCV to recognize number plates and Python Pytesseract to extract characters and numbers from the number plates to detect license number plates. OpenCV is an open-source machine learning library that offers a computer vision architecture. Pytesseract is a Tesseract-OCR Engine that reads picture types and extracts information from them.
7. Automatic Time Series Forecasting
Automatic Time Series Forecasting is a prediction of future values based on historical data. Consider how the price of your favorite stock fluctuates on a daily basis. Time-series forecasting may forecast a stock’s price across many time periods. For example, projecting Tesla’s stock price for the next 60 days or over longer time periods. Time-series data also includes weekly account signups, daily income, hourly transactions, and so on.
8. Barbie with Brains Project
The fast advancement of modern technology has provided the path for novel concepts, one of which is described in this article as “Barbie with Brains.” This Barbie is in contrast to the other dolls, which remain idle and may interact with humans, particularly children, in the same way, that any normal person would. This interactive Barbie gets more captivating with its astounding characteristics, such as Barbie being a knowledge hub for educational purposes, which aids youngsters in their schooling and learning, where there is sometimes no need for any information or teaching support when Barbie is around. Some of its remarkable physiognomy enables children to feel at ease with their own toys by establishing conversations, identifying faces, detecting emotions, and playing reassuring music and messages.
9. Build a Collaborative Filtering Recommender System in Python
When it comes to constructing intelligent recommender systems that can learn to deliver better recommendations as more user information is collected, collaborative filtering is the most commonly employed approach. Collaborative filtering is a technique that can filter out items that a user may be interested in based on the reactions of other users. It examines a big group of individuals and discovers a smaller collection of users with similar likes to a specific user. It considers the goods they like and combines them to get a ranked list of recommendations. There are several methods for determining which users are similar and combining their decisions to provide a list of suggestions.
10. Build a Similar Images Finder ML Project
In this project, you will create a similar image finder by analyzing the training weights of the image object-classifier VGG and utilizing it to extract feature vectors from an image database in order to determine which photos are “similar.” This strategy is known as transfer learning, and it involves no training on your part because the hard work was done when VGG was being trained, and you can simply re-use the taught weights to form a new model.
11. Build Classification Algorithms for Digital Transformation [Banking]
Bank XYZ has a rising client base, with the vast majority of them being liability customers (depositors) rather than borrowers (asset customers). The bank wants to swiftly grow the borrower base in order to bring in additional revenue through loan interests. The bank’s past quarter campaign had a single-digit conversion rate on average. The key strength of the company plan is digital transformation — creating successful campaigns with better target marketing to boost the conversion ratio to double digits with the same budget as the previous campaign. As a data scientist, you will be tasked with creating a machine learning model to identify potential borrowers in order to enable targeted marketing. Create a machine learning model to do targeted digital marketing by predicting who would convert from liability customers to asset customers.
12. Build CNN for Image Colorization using Deep Transfer Learning
The act of taking an input grayscale (black and white) image and creating an output colorized image that reflects the semantic colors and tones of the input is known as image colorization. Each pixel of a target grayscale image is allocated a color in image colorization. Image colorization is useful in the development of numerous applications such as medical microscopes, medical imaging, denoising and rebuilding old images, night vision cameras, and so on. For image colorization, for example, you can utilize autoencoders, a fully automatic data-driven approach. Autoencoders are a sort of feedforward neural network in which the input and output are the same. The VGG16 model will be used to extract features. VGG16 is a classic neural network that serves as the foundation for many computer vision applications. The goal of this project is to create a convolutional neural network that can best transform grayscale photos into RGB images.
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