Gaining insights into how top performers kick-started their careers in the technologies of Data Science and Machine Learning is beneficial to aspiring graduates, who are looking to get a foothold in this highly sophisticated and competitive sector.
Beginners can make giant strides in applying the various techniques of Machine Learning to solve real-time problems, through the use of cutting-edge Machine Learning projects, which are recommended by industry leaders.
These top-tier projects in Machine Learning offer learning in the central aspects of ML which include, supervised and unsupervised earning, neural networks, and deep learning. Learners can access these ML projects which incorporate real-world datasets in the public domain.
Industry experts stress the fact that beginner projects in ML should entail diverse commercial domains which as a result can lead to hands-on and exceptional job training. This training in turn will assist professionals to propel their careers in Machine Learning and data science through innovative methodologies.
The following are the top ten beginner projects on ML which are a perfect mix of the kaleidoscopic challenges that learners come across in their job roles as data scientists or machine engineers.
Top 10 Projects for Machine Learning Beginners
Sales Forecasting by deploying Wal-Mart Dataset
This is regarded as a common ML case example, which can help identify different factors that can sway the sales of products in addition to estimation of the future volume of sales. This ML project deploys the use of Wal-Mart data set with a sample for 98 products spread across 46 outlets. The objective of this ML project is to arrive at enhanced quality decisions which are data-driven, thus improving channel optimization. The Wal-Mart dataset can be used to generate a predictive model and estimate sales volumes.
BigMart Machine Learning Project
This is a gigantic sales dataset comprising 1559 products across 10 different branches in multiple cities. The required outcome of this ML project is to generate a regression model to forecast the sales performance of each product using specific attributes for each store and product. This Machine Learning model assists Big Mart to comprehend the various dynamics of products that play a significant role in enhancing the overall sales.
Music Recommendation ML project
This is a widely popular ML project which can be applied across multiple domains. You might have come across this ML project when using an e-commerce site or music website. Amazon deploys this technique at the checkout phase in the form of product recommendations that are similar to your chosen product. Similarly, Netflix and other media stream services such as Spotify will recommend analogous songs or movies based on the content that you‘ve watched previously.
This ML project is used to enhance the recommendation qualities of media consumption systems. Learners of this ML project will get knowledge inputs to determine the possible choices of movies or songs taking into account the different factors like media repetition and time frame.
Recognition of Human Attributes via Smartphone Dataset
This project incorporates a dataset consisting of physical fitness recordings of a sample of 30 people, using inertial sensor technology stored on the smartphone. The end goal of this Machine Learning technique is to accurately calculate the significant parameters of human activity, which can lead to enhanced fitness. Beginners can mine valuable knowledge inputs by studying these ML projects, as it helps them to analyze and solve the multi-categorization using cutting-edge source code.
Stock Market Predictor through Time Series
The majority of corporate Wall Street stock exchanges deploy this sophisticated data science project, which makes this an excellent project to gain expertise on for Machine Learning beginners, This interesting ML projects focuses on the finance domain and uses a stock price forecaster system that observes and learns the core performance parameters of the company, and then successfully predicts the future stock fluctuation to a high degree of precision.
Certain challenges need to be straightened out- such as the granularity of the stock price data, index volatility, global economic effects on financial indicators. But the great thing about using this Machine Learning project-is that the shortened feedback cycle can make it easier for financial experts to assess and continuously modify their prediction as and when the new data comes.
Wine Quality Prediction Dataset
It a commonly known fact that the taste of the wine increases exponentially with time. Apart from this, several other factors come into play such as acidity, alcohol quantity, density which determine wine quality certification. The ultimate objective of this Machine Learning project is to generate a robust model to forecast the wine quality by doing a comprehensive analysis of the different chemical properties. The dataset of this ML model comprises nearly 5000 observations with different independent variables.
Classification of MNIST Handwritten Digits
Neural networks and deep learning are central to automatic text generation and self-driving cars. Learners need to start with a flexible and manageable dataset similar to the MNIST data set. Though this data set is too minute to be placed into the PC memory, the ML learning techniques related to handwritten digit recognition can present challenging learning outcomes that will be helpful to build a robust understanding of Data Science and Machine Learning.
Building Recommender System suing MovieLens Dataset
A movie recommender system needs to be efficient to gain traction with the ever-demanding audience searching for customized content. These popular datasets are deployed by infamous streaming media organizations such as Hulu and Netflix. The data set of this Machine learning comprise of approximately 4000 movies and over 10 million movie ratings gathered from 6000 MovieLens users.
Boston Housing Cost prediction Machine Learning project
This Machine Learning project uses dataset parameters such as crime rate, demographics of residents, and presence of a non-retail business. This is a publicly available ML project, which is used to forecast the housing prices across different residential areas in Boston.
The principal objective of this ML project is to accurately predict the final selling prices of a brand new home by the application of fundamental ML concepts to the historical housing prices data. This data set can be considered comparatively small with over 500 observations and can be used as a good launching pad for amateurs in ML concepts, who can later pick up valuable learning on regression concepts.
Analysis of Social Media sentiments by deploying Twitter Dataset
Humongous data is generated through social media platforms of the likes of Facebook, Twitter, and Reddit. This big data needs to be mined in various commercial ways by the companies to derive profits. Sentiment analyzer in ML projects is used to comprehend opinions, public perceptions which in turn can be used to conduct branding of the business. Various sentiments can be garnered using Machine Learning by categorizing them as content pieces. One prime example would be using AI in parsing the Twitter datasets to get acquainted with a rich variety of tweet contents including metadata such as location, hashtags, and retweets.