Machine learning, as a field, is growing at a breakneck speed. Github is that whiteboard which the whole world is watching. Top quality code is being regularly posted on that infinite board of wisdom. It is obviously impossible to track all things that go on in the world of machine learning but Github has a star-rating for each project.

machine learning final year projects github

Basically, if you star a repository, you show your appreciation for the project as well as keep track of repositories that you find interesting.

This star rating then can be one of the good metrics to know the most followed projects. It provides an application programming interface API for Python and the command line. It is useful for recognising and manipulating faces in images. The deep-learning model has an accuracy of This library can also handle real-time face recognition. It is lightweight and allows users to learn text representations and sentence classifiers.

It works on standard, generic hardware. Models can be reduced in size to even fit on mobile devices. Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. The goal of text classification is to assign documents such as emails, posts, text messages, product reviews, etc… to multiple categories.

machine learning final year projects github

It is a very useful resource for NLP enthusiasts. This is a collection of resources that help you understand and utilise TensorFlow. The github repo contains a curated list of awesome TensorFlow experiments, libraries, and projects. TensorFlow is an end-to-end open source platform for machine learning designed by Google. It has a comprehensive ecosystem of tools, libraries and community resources that lets researchers create the state-of-the-art in ML. Using it developers can easily build and deploy ML powered applications.

Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. Users can use this framework to build real-world ML apps, deploy and test them. It even supports event collection, evaluation, and querying predictive results. It is based on scalable open source services like Hadoop, HBase etc.

This repository is slightly different from all of the above as it has been shut down due to lack of funds! It is quite an interesting concept where AI is used to color images.University management platform dedicated for post-graduation in computer science field using django rest framework. Refocus an image just by clicking on it with no additional data.

A flask app that lets you automate the examination system by composing question-answer pairs and evaluating candidate responses without any human intervention in an efficient and automatic way. Final Year Project, based on the idea of analysing and predicting cryptocurrency prices.

Handwritten character recognition system written in Java Neural network. The purpose of this project is to take handwritten digits as input, process the digits, train the neural network algorithm with the processed data, to recognize the pattern and successfully identify the test digits. A multipurpose Wireless Surveillance Rover an Electronics project made with arduino. Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli.

In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours. Add a description, image, and links to the final-year-project topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the final-year-project topic, visit your repo's landing page and select "manage topics.

Learn more. Skip to content. Here are 85 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests.

Latest Machine Learning Projects to Try in 2019

Predict Cryptocurrency Price with Deep Learning. Updated Mar 1, Jupyter Notebook. Updated Oct 19, Java. Updated Apr 10, Scala. Updated Oct 22, Python.It is based on modern methods of Machine learning and Deep learning. This project does not use physico-chemical modellings but the history of the weather data. After identifying meteorological sources from which data from more than 10 years ago could be extracted, students developed algorithms allowing to extract the meteorological data.

MongoDB was chosen as a database for its capacity to manage geographical coordinates. The next phase consisted in the cleaning of the data to make them exploitable for Machine Learning. They gave encouraging results. Machine Learning algorithms are running to put the mails into clusters, each cluster belonging to a user project. For example, if a person is working on 5 projects, there will be 5 clusters.

Mails are imported to a Neo4j Database. Once connected, the user can choose between the clusters. The goal of this project is to implement some fairly advanced machine learning algorithms in order to detect the overall meaning of financial news without having to read them.

The project is structured in different parts. The first part is the web scraping. Using different tools like Tor it is possible to download automatically thousands of articles from different websites like Bloomberg or CNN and to archive them in different folders corresponding to different companies.

The next step is labelling. This operation is needed to calibrate the dataset in order to implement correctly the learning part. This is not a computed operation, it must be done by humans so that the overall sense of each article is understood and the data set is divided in four categories: positive, negative, neutral and irrelevant.

Then articles are parsed so that all the useless words are deleted and only nouns, verbs, adjectives survive, making the dataset analysis easier. This is when the learning phase begins. The algorithm analyses the articles and builds the dictionaries with the words it found in the training set.

The test set compares the words contained in the new articles with the labelled ones and assigns a label to the new article. Another technique used is the logistic regression which is more accurate because it gives more importance to more frequent words. The environment used in this project is Python which is a versatile language and has a lot of libraries already available to perform natural language processing.One of CS's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research.

The final project is intended to start you in these directions. For group-specific questions regarding projects, please create a private post on Piazza. Please first have a look through the frequently asked questions. Note : only one group member is supposed to submit the assignment, and tag the rest of the group members do not all submit separately, or on the flip side forget to tag your teammates if you are the group's designated submitter. If you do not do this, you can submit a regrade request and we will fix it, but we will also deduct 1 point.

Note : for Springwe require that you submit either the proposal or milestone. You do not have to submit both, but you are more than welcome to if you'd like. If we deem necessary, we may require you meet with a TA to discuss your project before the final report. Your first task is to pick a project topic. If you're looking for project ideas, please come to project office hours, and we'd be happy to brainstorm and suggest some project ideas. In the meantime, here are some suggestions that might also help.

Most students do one of three kinds of projects: Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it. Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.

Theoretical project. This is often quite difficult, and so very few, if any, projects will be purely theoretical. Some projects will also combine elements of applications, algorithms and theory. Many fantastic class projects come from students picking either an application area that they're interested in, or picking some subfield of machine learning that they want to explore more.

So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you're excited about. Just be sure to ask us for help if you're uncertain how to best get started. Alternatively, if you're already working on a research or industry project that machine learning might apply to, then you may already have a great project idea.

A very good CS project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent machine learning research papers. Finally, looking at class projects from previous years is a good way to get ideas.

We still expect a solid methodology and discussion of results, so pace your project accordingly. Notes on a few specific types of projects: Deep learning projects : Since CS discusses many other concepts besides deep learning, we ask that if you decide to work on a deep learning project, please make sure that you use other material you learned in the class as well.

For example, you might set up logistic regression and SVM baselines, or do some data analysis using the unsupervised methods covered in class. We may grade these projects using different criteria to make sure that grading is fair for students who have not had exposure to DL before. Finally, training deep learning models can be very time consuming, so make sure you have the necessary compute.

Preprocessed datasets : While we don't want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets e.Github has become the goto source for all things open-source and contains tons of resource for Machine Learning practitioners.

We bring to you a list of 10 Github repositories with most stars. We have not included the tutorial projects and have only restricted this list to projects and frameworks. TensorFlow is an open source software library for numerical computation using data flow graphs.

Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays tensors that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit. The system is general enough to be applicable in a wide variety of other domains, as well.

The project was started in by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. It is currently maintained by a team of volunteers. It was developed with a focus on enabling fast experimentation.

10 most popular Machine Learning Projects on Github

Being able to go from idea to result with the least possible delay is key to doing good research. Apache PredictionIO incubating is an open source machine learning framework for developers, data scientists, and end users. It can be trained to recognize other languages. MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

A graph optimization layer on top of that makes symbolic execution fast and memory efficient. Swift AI includes a set of common tools used for machine learning and artificial intelligence. These tools are designed to be flexible, powerful and suitable for a wide range of applications.

Bhasker is a Data Science evangelist and practitioner with proven record of thought leadership and incubating analytics practices for various organizations. With over 16 years of experience in the area of Business Analytics, he is well recognized as an expert within the industry. He is B. Tensorflow TensorFlow is an open source software library for numerical computation using data flow graphs. Bhasker Gupta Bhasker is a Data Science evangelist and practitioner with proven record of thought leadership and incubating analytics practices for various organizations.

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machine learning final year projects github

Previous Article Finance Ministry to hire IT professionals to support development of big data architecture. Our Upcoming Events.Machine Learning is clearly a field that has seen crazy advancements in the past couple of years. This trend and advancements have created a lot of Job opportunities in the industry.

The need for Machine Learning Engineers are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. Collecting Data: This stage involves the collection of all relevant data from various sources. Analyze Data: Data is analyzed to select and filter the data required to prepare the model. Test Model: The testing dataset determines the accuracy of our model.

Deployment: If the speed and accuracy of the model are acceptable, then that model should be deployed in the real system. After the model is deployed based upon its performance the model is updated and improved if there is a dip in performance the model is retrained.

Supervised Learning: It is the one where you have input variables x and an output variable Y and you use an algorithm to learn the mapping function from the input to the output. So it becomes difficult to classify that data in different categories. Unsupervised learning helps to solve this problem. This learning is used to cluster the input data in classes on the basis of their statistical properties. Reinforcement Learning: It is all about taking appropriate action in order to maximize the reward in a particular situation.

The reinforcement agent decides what actions to take in order to perform a given task. In the absence of a training dataset, it is bound to learn from its experience. Having a revenue of more than a Billion Dollars, the company has decided to launch a new reality show: RJ Star. Response to the show is unprecedented and the company is flooded with voice clips.

The whole success of the show and hence the profits depends upon quick and smooth execution. Business Challenge: Lithionpower is the largest provider of electric vehicle e-vehicle batteries. Drivers rent battery typically for a day and then replace it with a charged battery from the company. As the life of a battery depends on factors such as overspeeding, distance driven per day, etc.

You as an ML expert have to create a cluster model where drivers can be grouped together based on the driving data. Business Challenge: BluEx is a leading logistics company in India. However, BluEx is facing a challenge where its van drivers are taking a suboptimal path for delivery.

This is causing delays and higher fuel cost. You as ML expert have to create an ML model using Reinforcement Learning so that efficient path is found through the program.Post a Comment. Hello, Welcome to Final Year Projects.

TOP 50 Best Artificial Intelligence Projects GitHub In April, 2020

Hope you will enjoy the FREE projects. Latest Projects. Post Top Ad. Machine Learning is a branch of Artificial Intelligence which is also sub-branch of Computer Engineering. According to Wikipedia"Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed". The term "Machine Learning" was coined in by Arthur Samuel. If you are looking for Machine Learning project ideasthen you are at right place as this post has many ideas for your first Machine Learning project.

If you have any idea in mind, please comment it and we would add it to this list. You can also subscribe to Final Year Project's by Email for more projects and seminar on Machine learning. Tweet Share Pin it Comment. About Admin. No comments:. Newer Post Older Post Home.

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machine learning final year projects github

Author Details Final-yearproject. All the resources in this website are Free of charge. This website is built by the students and is for the students. Follow by Email. Wearable Bio Sensors.

The use of wearable mo Looking for final year projects for Computer Science Engineering? Looking for latest Computer Science projects? If your answer is yes and Java based Project on Airline Reservation.

When it comes to planning for your final year projectit becomes very tricky as there are so many topics to choose from. In this article, Computer Engineering or CSE has become number 1 choice of students in recent years.


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