Machine Learning Projects for Beginners
There is probably no one who hasn’t heard of Artificial Intelligence. AI was once compared to the discovery of fire, a discovery which changed human race forever. Similar to fire, AI has permeated every part of our lives and is changing it for the better.
Machine learning is a branch of AI; it’s all about creating an algorithm, analyzing data, learning from data, processing data, identifying and applying patterns on data with minimal intervention by humans.
Moving towards the definition of Machine Learning, “Machine Learning is the application or branch of Artificial Intelligence (AI) that is the ability to learn from data, train data, identify patterns, and improve overall user experience. It focuses on developing the computer program which can easily analyze the data.”
Machine learning and its projects are fascinating because it involves real-time data analysis, management, and learning data. It helps to solve real-time and human-related problems. It can be said that a machine learning program is the program that writes the other program, and then they write another; this process is continuous and never-ending.
As a programmer, you are probably fascinated by its wide category of problem statements and state of the art solutions. It involves image classification, image detection, image recognition, voice recognition, and many other study fields. While dealing with the problem statement, you need to understand the problem, recognize its algorithm, develop the most suitable set of techniques, and apply it to large sets of data with different problems with a little bit of tweaking.
When you go for a more practical approach, everything out there becomes more interesting and easier to learn. As a beginner, you should start with some basic projects so that you can brush up your skills and get in-depth knowledge of the required algorithm.
Some features of a Machine Learning Project:
Some key points to remember before moving toward the machine learning project:
Some projects based on Machine Learning:
These small-scale projects will help you create your base and develop an understanding of the fundamentals of machine learning. Before moving towards big datasets, one should be familiar with working with a small dataset and create a graph and learning curve.
Wine Quality Test Project: Here, you have to understand the chemical composition of the mixture, how the wine is made, and then you have to apply the machine learning model on the data to obtain the quality of the wine.
The data source you can refer to:
Wine quality: This dataset is composed of the different qualities of wine and their chemical composition. There are 2 datasets that contain red and white wine data samples from the north of Portugal.
Fake News Detection: Social media has contributed to the proliferation of fake news. It is really very hard to understand the quality and correctness of the content present in social media. According to surveys, 3 out of 5 messages in social media are fake. Using this model you can understand the ambiguity of the news present in our world.
Fake news is like wildfire, and spreads uncontrollably.
The data source you can refer to:
Fake news dataset: find out the data present in social media, which is fake and predict data or information that is the legitimate source.
Kinetics project: This project identifies human actions and reactions by observing their behavior during activities. This dataset contains 3 different datasets, each of which is kinetic with a different collection of URLs and high-quality images and videos.
The data source you can refer to:
Kinetics Dataset: This contains about 650,000 video clips with 400-600-700 different classes of human action divided into subclasses, with different data set versions.
Any ML project should be interesting, true-to-life, and meaningful. When you try to understand the basics of any technology, you must work on it hands–on to understand and take a deep dive into the subject. Here we will try to cover machine learning projects, which can be a great starting point for you to learn about machine learning, or which can be added to your portfolio of projects to make your resume stand out.
Sales forecasting data increases day by day and minute by minute, and this is a good place to apply machine learning and data analysis. It is very helpful in practising data visualization, analysis, and exploratory analysis.
Data sources you can refer to:
Stock price predictions: The stock market exchange is a candy shop for data scientists who are interested in the finance sector. There are numerous data sets that you can choose from and perform analysis on.
You can apply predictions on the prices, fundamentals, value investing, and future forecasting and arbitraging.
Data sources you can refer to:
Human Activity Recognition with Smartphones Data: It is a classification problem where the sequence of accelerometer data has been recorded by the specialized harnesses or smart phones into known well-defined movements. For more information on the project and to develop more insights, you can visit the tutorial and then move onto the project. To visit the tutorial, click here. Human Activity Recognition is where you find what the person is doing and trace their activity and perform analysis and exploration of the data set.
The data source you can refer to:
Investigation on Enron data: It was the largest corporate meltdown in history. In the year 2000, they were called out for fraud. But luckily, for us, their database, which contains 500 thousand emails between employee, senior executive, and customers is still available. Data scientists have been using that data for education and research purposes for years.
The data source you can use:
Chatbot Intents Dataset: this is a basic machine learning project which you can undertake to develop a better understanding of the libraries and natural language processing. It contains the JSON file structure, which will respond to your chat with a defined pattern and syntax.
This is a useful machine learning project for beginners with source code in Python.
The data source you can refer to:
Flickr 30K Dataset: Flickr is a platform that provides an opportunity to upload, organize, and share your photos and videos. Flickr contains a 30k dataset; it has become a standard benchmark for sentence-based image processing.
It contains about 158k captions and 244k coreference chains. This is used to create a more accurate model.
The data source you can refer to:
Flickr image source by Kaggle: this paper contains records from Flickr, which has a 30k image dataset, captions, and co–references.
Emojify: (helps in creating your emoji with the help of Python) This performs a mapping operation between facial expressions and emojis. You are required to create a neural network to recognize the facial expression and map it down into the expression.
An emoji or avatar indicates a non-verbal cue; these cues are increasing as a part of our chatting and messaging world. It is used to describe your emotion, behavior and mood in your conversation.
The data source you can refer to:
Mall customer dataset: The mall customer dataset contains all the entries about the customers visiting the mall, their names, age, gender, recommendations, a product they buy, issues they face etc. Using the data’s different characteristics, we can gain insights into the data and divide the data into different attributes and group them into different groups, based on their behaviour.
The data source you can refer to:
Boston Housing: the most famous and used dataset is the Boston housing dataset; many machine learning tutorials take this dataset as an example dataset. This is used for pattern recognition; it contains 500+ observations with 14 attributes or distribution variables.
The common logic behind this project is to predict the new house’s cost using the regression model of machine learning.
The data source you can refer to:
Boston Housing Dataset: The dataset is the natural dataset, which is being collected by the US service and housing management system.
MNIST Digit Classification: MNIST stands for Modified National Institute of Standards and Technology; it is the dataset of 60+ thousand grayscale images of handwriting. In this project, you’ll be able to recognize the handwriting digits using simple Python and machine learning algorithms. This is very useful in computer vision.
As this dataset contains flat and relational data, this data is the best fit for beginners to learn more about the algorithmic strategy.
The data source you can refer to:
Digital handwriting recognition: here, you can easily find the pre-requisites for project development. The Machine Learning model is trained using Convolutional Neural Network, best known as CNN’s. This data set is the best fit for users dealing with less memory space.
Source code:
Handwriting recognition: this drive contains the complete source code of the project.
Conclusion
Machine Learning automates analytical modelling and building decisions. You can opt for different free or premium courses, which help you understand the space and create your projects.
Aforementioned are the collection of top machine learning projects available online, which are easy to use and develop. The project contains complete guidelines you can refer to. This will help you to learn new algorithms and master your machine learning skills.
If you want to gain expertise, dive into the concept and figure out how the module works.
Machine learning is the future and if you have set yourself up for a career in this space then building a solid resume with a project portfolio is the right way to go about it.
Wine Quality Test Project: Here, you have to understand the chemical composition of the mixture, how the wine is made, and then you have to apply the machine learning model on the data to obtain the quality of the wine.
Wine quality: This dataset is composed of the different qualities of wine and their chemical composition. There are 2 datasets that contain red and white wine data samples from the north of Portugal.
Fake News Detection: Social media has contributed to the proliferation of fake news. It is really very hard to understand the quality and correctness of the content present in social media. According to surveys, 3 out of 5 messages in social media are fake. Using this model you can understand the ambiguity of the news present in our world.
Fake news dataset: find out the data present in social media, which is fake and predict data or information that is the legitimate source.
Kinetics project: This project identifies human actions and reactions by observing their behavior during activities. This dataset contains 3 different datasets, each of which is kinetic with a different collection of URLs and high-quality images and videos.
Kinetics Dataset: This contains about 650,000 video clips with 400-600-700 different classes of human action divided into subclasses, with different data set versions.
Stock price predictions: The stock market exchange is a candy shop for data scientists who are interested in the finance sector. There are numerous data sets that you can choose from and perform analysis on.
Human Activity Recognition with Smartphones Data: It is a classification problem where the sequence of accelerometer data has been recorded by the specialized harnesses or smart phones into known well-defined movements. For more information on the project and to develop more insights, you can visit the tutorial and then move onto the project. To visit the tutorial, click here. Human Activity Recognition is where you find what the person is doing and trace their activity and perform analysis and exploration of the data set.
Investigation on Enron data: It was the largest corporate meltdown in history. In the year 2000, they were called out for fraud. But luckily, for us, their database, which contains 500 thousand emails between employee, senior executive, and customers is still available. Data scientists have been using that data for education and research purposes for years.
Chatbot Intents Dataset: this is a basic machine learning project which you can undertake to develop a better understanding of the libraries and natural language processing. It contains the JSON file structure, which will respond to your chat with a defined pattern and syntax.
Flickr 30K Dataset: Flickr is a platform that provides an opportunity to upload, organize, and share your photos and videos. Flickr contains a 30k dataset; it has become a standard benchmark for sentence-based image processing.
Flickr image source by Kaggle: this paper contains records from Flickr, which has a 30k image dataset, captions, and co–references.
Emojify: (helps in creating your emoji with the help of Python) This performs a mapping operation between facial expressions and emojis. You are required to create a neural network to recognize the facial expression and map it down into the expression.
Mall customer dataset: The mall customer dataset contains all the entries about the customers visiting the mall, their names, age, gender, recommendations, a product they buy, issues they face etc. Using the data’s different characteristics, we can gain insights into the data and divide the data into different attributes and group them into different groups, based on their behaviour.
Boston Housing: the most famous and used dataset is the Boston housing dataset; many machine learning tutorials take this dataset as an example dataset. This is used for pattern recognition; it contains 500+ observations with 14 attributes or distribution variables.
Boston Housing Dataset: The dataset is the natural dataset, which is being collected by the US service and housing management system.
MNIST Digit Classification: MNIST stands for Modified National Institute of Standards and Technology; it is the dataset of 60+ thousand grayscale images of handwriting. In this project, you’ll be able to recognize the handwriting digits using simple Python and machine learning algorithms. This is very useful in computer vision.
Digital handwriting recognition: here, you can easily find the pre-requisites for project development. The Machine Learning model is trained using Convolutional Neural Network, best known as CNN’s. This data set is the best fit for users dealing with less memory space.
Handwriting recognition: this drive contains the complete source code of the project.
Research & References of Machine Learning Projects for Beginners|A&C Accounting And Tax Services
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