Machine Learning is one of the in-demand technologies preferred by most engineering students who have a knack for programming and foresee their career in AI and Machine Learning domain. For any technology, no theoretical knowledge can replace hands-on practical knowledge and that is the reason why the best IT training companies are now embracing a project-based training approach.
Projects help you understand the practical implications of the learnt concepts and master it, which through books and training manual becomes quite a fantasy. Machine Learning projects familiarize you with the technology applications, real-time implementation challenges and help you explore the topics in deep and improve your applied Machine Learning Skills.
The article walks you through the most popular and engaging Machine Learning projects that can help you as beginners solidify your knowledge to the core and scale up to the next level of machine learning training and certification courses. Being the best investment that you make in carving your machine learning career, these machine learning projects will help you in learning all the practicalities that you need to succeed in your career and to make you employable in the industry.
Owing to the increasing demand from modern consumers for customized content, there is a strong need for machine learning students to discover how Netflix, Hotstar and Prime Video make movie and web series recommendations. One of the popular datasets available over the internet for beginners that can support in building a movie recommendation system is Movielens Dataset. This dataset includes more than 1,000,210 ratings for 4000 movies made by Movielens users, which can help you create a world-cloud visualization of movie titles to devise an effective movie recommendation system.
This is another great machine learning project that can help data scientists and machine learning engineers to make their way to the finance field. Stock Prices Predictor system is software that analyzes a companys performance over a given period and predicts the future stock prices. Here you get to work on the granular stock prices data, volatile indices and also gain real-time experience of understanding macroeconomic and fundamental indicators that cause high fluctuations in stock prices. With shorter feedback cycles in financial markets, working on stock market data and validating the new price predictions become easy for the data scientists. Based on fundamental indicators from the companys quarterly report and analyzing 6-month price movements, you may start with a simple machine learning program and predict the new stock prices.
Social media data generated in huge volumes through diverse platforms such as Twitter, YouTube, Facebook etc. is of extreme importance in marketing and business branding. Mining this social media data can help in understanding public sentiments, trends and opinions. A sentiment analyser system considers different content pieces picked up from emails, tweet, social media posts through machine learning algorithms and predict further. For beginners, Twitter data is the best to start with and practice sentiment analysis machine learning use cases. The project becomes engaging with tweet contents comprising metadata, hashtags, location, users, retweets and more to help draw insightful analysis. Working with the twitter dataset will help you understand the challenges associated with social media data mining and also learn about classifiers in depth. The foremost problem that you can start working on as a beginner is to build a model to classify tweets as positive or negative.
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Neural networks and deep learning are two emerging branches of modern artificial intelligence, which have led to major progress in image recognition, automatic text generation, and even in self-driving cars. To learn this, you need to begin with a fairly manageable dataset. The MNIST Handwritten Digit Classification Challenge is the perfect entry point. Image data is generally harder to work with than flat relational data, for this reason, The MNIST data is quite beginner-friendly. It will be challenging but not undoable as it doesn't need high computational power. Enrol now for the most desirable project-based machine learning training to master solving image recognition problems
The success rate of a movie is largely dependent on peoples perception and opinions, through word-of-mouth. In todays digital world, word-of-mouth is prevalent in the form of reviews found online. The opening day and the first few weeks following a movies release is crucial, and hence production houses place a lot of importance on moviegoers opinions and develop trailers and publicity strategies to sway public opinion. Taking this into account, the project concentrates on building a review system for predicting the success rate of a movie.
Companies that depend on credit card transactions need to find anomalies in the system, for which this credit card fraud detection model can be helpful. This project aims to use the transaction and their labels as fraud or non-fraud to detect if new transactions made from the customer are fraud or not.
The Uber Data Analysis project is an excellent way to learn and perform data visualization. The Uber dataset contains data about 4.5 millions of uber pickups, and the project requires you to represent this rides data in a way to help make further improvements in the business.
Distinguishing the fake news from the real ones is the ultimate goal of this Fake news detection project that can help you solidify supervised learning concepts and implement this model.
House Prices Dataset consists of housing prices for different locations of a particular region. The dataset encompasses the 14 other attributes including information about house/building age, crime rate, people age etc. The objective of this machine learning project is to predict the selling price of a new home by applying basic machine learning concepts on the housing prices data. The project is appropriate for machine learning beginners to gain hands-on practice on regression concepts.
It is known to all that older the wine, the better is the taste. However, not just age but wine quality testing and certification take in many more factors dependent on physiochemical tests to check fixed and volatile acidity, density, PH levels and more. The primary objective of this Wine Quality test project is to build a machine learning model to predict the quality of wines by exploring their various chemical properties.