#NLP Projects

[techieyan]

Revolutionizing Text Analysis with NLP Projects in Artificial Intelligence

The field of artificial intelligence (AI) has seen tremendous growth and development in recent years, with advancements in machine learning, deep learning, and natural language processing (NLP). NLP, in particular, has revolutionized the way we analyze text data, providing powerful tools and techniques for extracting insights and meaning from large volumes of text.

NLP is a branch of AI that focuses on the interaction between computers and human language. It enables computers to understand, interpret, and generate human language, allowing them to process and analyze text data in a similar way to how humans do. With the increasing amount of unstructured data in the form of text, such as social media posts, customer reviews, and news articles, NLP has become an essential tool for businesses and organizations looking to gain valuable insights from this data.

One of the most significant applications of NLP in AI projects is sentiment analysis. Sentiment analysis is the process of identifying and extracting emotions, opinions, and attitudes from text data. With the help of NLP techniques, sentiment analysis can accurately identify the sentiment expressed in a piece of text, whether it is positive, negative, or neutral. This is particularly useful for businesses as it allows them to understand how their customers feel about their products, services, and brand, and make data-driven decisions to improve their offerings.

Another NLP project that has revolutionized text analysis is named entity recognition (NER). NER is a technique that identifies and classifies named entities in text, such as people, places, organizations, and dates. It enables computers to understand the context of a text and extract relevant information, making it an essential tool for tasks such as information extraction, question-answering, and document summarization.

NLP also offers powerful tools for text classification, which involves categorizing text into predefined categories. This is useful for tasks such as spam detection, topic classification, and sentiment analysis. With the help of NLP techniques, computers can learn to classify text accurately, saving businesses and organizations time and resources in manual classification.

One of the most exciting NLP projects in AI is natural language generation (NLG). NLG is the process of generating human-like text from data, making it possible for computers to write articles, reports, and summaries automatically. This has significant implications for various industries, such as journalism, content creation, and customer service. With NLG, businesses can generate personalized content for their customers and automate routine tasks, freeing up human resources for more complex tasks.

NLP has also made significant contributions to the field of machine translation, allowing computers to translate text from one language to another accurately. With the help of NLP techniques, machines can understand the context and nuances of different languages and produce accurate translations. This has opened up new opportunities for global businesses to expand their reach and communicate with customers in their preferred language.

In addition to these applications, NLP has also been used in AI projects for text summarization, question-answering, and text-to-speech conversion. These applications have not only improved the efficiency and accuracy of text analysis but also opened up new possibilities for businesses and organizations to leverage the power of NLP in their operations.

In conclusion, NLP has played a significant role in revolutionizing text analysis in AI projects. Its ability to understand and analyze human language has enabled computers to extract valuable insights, information, and meaning from large volumes of text data. With the continuous advancements in NLP, we can expect to see even more impressive applications that will further enhance the capabilities of AI in text analysis. As businesses and organizations continue to generate and collect vast amounts of text data, NLP will become an increasingly crucial component of AI projects, paving the way for a more efficient, accurate, and intelligent future.

[anonymousdormhacks]

Google says alexander "cheated on his wife" hamilton rights ig

[detroitography]

Detroit by the Numbers: State of the City 2014 - 2025

by: Ted Tansley, Data Analyst Mike Duggan’s tenure as Mayor of Detroit has been focused on data. The number of residents in the city, number of demolitions, number of jobs brought to the city, number of affordable housing built or preserved. All data points get brought up in his yearly State of the City address where he makes his case to the public that he and his team have been doing a good job…

[manmishra]

AI Integration in Software Development Explained

Dive into the future of software development with our latest article on AI Integration in Software Development! Discover how AI is transforming code generation, testing, and project management, enhancing productivity and collaboration. Don't miss out—read

Artificial Intelligence (AI) is transforming the software development landscape. It enhances productivity, accuracy, and innovation across various stages of the development lifecycle. This article explores how AI integrates into software development. It examines its benefits, challenges, and practical applications. The article includes code snippets to illustrate AI’s capabilities. Key Areas of…

[pythonjobsupport]

TWITTER SENTIMENT ANALYSIS (NLP) | Machine Learning Projects | GeeksforGeeks

Dive into the language of social media with this exciting episode of our Machine Learning Project Series! Here, we unravel … source

[loveinthetimeofanarchy]

not to be a complete hater but using machine learning to translate video of sign language into english text is something that college students have been making successful projects of for at least 10 years. the fact that none of these tools has entered any sort of zeitgeist is a problem of economic prioritization

[fandomtrumpshate]

FTH 2025 Donation Totals

Friends, this has been an extraordinary year for the auction. We know how and why this happened: like in early 2017, everyone is scared and upset and looking for a way to do something meaningful.

And—just like in 2017, and every year since—hundreds of us have stepped up to support our most vulnerable neighbors and the organizations working to protect them.

Except this year, we did it on a scale we've never done before.

Last year, our donation total was an incredible $67,776.28

This year's donation total...

are you ready for it....

(you're not ready for it. we weren't.)

This year's donation total is:

Yes, you're reading that right. $127,204.11

We're flummoxed too—and deeply grateful to everyone who has poured their time and effort and money and love into participating in the auction this year, and into the fanworks that will come from it.

If you're curious about how those donations were distributed across the different organizations, here is the breakdown (this breakdown doesn't include employer match donations, which is why the total is a little lower):

Bellingcat: $2,636.19

Congo Leadership Initiative: $2,842

Crips for esims for Gaza: $4,762.60

Disability Law United: $3,835.39

Environmental Integrity Project: $3,712

Fight for the Future Education Fund: $3,108

Freedom to Read Foundation: $7,139.50

Global Project Against Hate and Extremism: $6,473

Hope for Ukraine: $12,613.93

In Our Own Voice: National Black Women's Reproductive Justice Agenda: $3,626

Middle East Children's Alliance: $13,572.43

National Network to End Domestic Violence: $4,999.95

Never Again Action: $4,555

NLP: $3,745.16

Young Center for Immigrant Children’s Rights: $10,072.56

Umbrella: organizations serving vulnerable LGBTQ people

Brave Space Alliance: $2,558

Kentucky Health Justice Network Inc: $2,636

Sherlock's Homes: $7,780.77

TransFamily Support: $5,387.01

TransQueerPueblo: $2,949

Other local LGBT organizations: $10,696.71 Yes, you're reading that right again. Three orgs AND the cumulative Other local LGBT orgs broke five figures.

We're especially delighted because, for the first time, the umbrella category worked the way we've always hoped it would! As you can see above, significant numbers of people used the umbrella category as a way to connect to an organization local to them.

As we learned through people's comments on the donation form, some people donated to organizations they were already familiar with (and in some cases had already donated to, or even volunteered at); others used this as a reason to learn more about their local organization and support them.

We'll share more about the "other umbrella" donations over the next few weeks—some more detailed stats, as well as the names of some of the local orgs that people donated to—and we'll invite those of you who connected up with local orgs to share your stories. We love that so many people took this chance to support groups working in their own community, and we hope that we can keep that going next year and beyond!

And speaking of the future!

Now is a great time to follow @fth2025fanworks. We'll use that blog to share any auction fanwork that gets posted to tumblr.

We urge you to keep up with the organizations you supported this year (and the others on our list!) Follow them on social media, subscribe to their newsletters, whatever works best for you. It will enable you to keep an eye on the good work you've helped support, and to find out quickly when these orgs need some extra support, financial or otherwise.

And if you're looking out at the world and feeling the itch to do more, here are some possibilities:

Follow @fthaction, the meatspace activism wing of FTH. We relaunched this project in the weeks between the end of signups and the beginning of browsing period, sharing some reading lists, an individualized activism bingo card, and an AMA with activist and organizer Kat Calvin. (We also did a test-flight AMA with ourselves, talking about the auction.) We'll probably need some time to recover from this year's auction, but we'll be back soon with more resources to share, more AMAs, and more tools for exploring all the different forms that meaningful activism can take and for figuring out which ones are right for you.

Organize your own auction! We've put together a detailed playbook that contains that contains as much information and as many resources as we can provide for getting an auction off the ground, including detailed guides. Almost everything in the playbook is fully public; there are a few forms that are access-locked because google has stupid ideas about sharing forms, but we're happy to give you access to those, too: just drop us an email.

Over here at FTH headquarters we are all in need of a long nap. But we'll be back in a couple of weeks, as promised, to share more about the umbrella orgs and to dig back into @fthaction to see what's possible.

Looking forward to a whole bunch of new fanworks! <3 your FTH mods

[datascienceassignmenthelp]

New Era of Natural Language Processing

[datascienceassignment]

Excel in your machine learning projects with expert guidance. Explore our comprehensive machine learning project help services at DataScienceAssignment.com to achieve success.

[techieyan]

From Concept to Completion: How to Choose and Execute an AI Project for Your Final Year

Artificial Intelligence (AI) has become a buzzword in the tech industry, with its potential to transform and revolutionize various sectors. As a final-year student, you may be considering an AI project for your final year. However, with the vastness and complexity of the subject, it can be challenging to know where to begin. In this article, we will guide you on how to choose and execute an AI project for your final year.

1. Identify Your Interest and Goal

The first step in choosing an AI project is to identify your interests and goals. AI is a vast field with numerous subfields such as machine learning, natural language processing, computer vision, and robotics. Each subfield has its own set of techniques, algorithms, and applications. Therefore, it is essential to have a clear understanding of what interests you the most and what you want to achieve through your project.

2. Research Existing Projects

Once you have identified your interest and goal, the next step is to research existing AI projects. This will help you understand the current trends, techniques, and applications in your chosen field. It will also give you a better idea of what has been done before and what gaps you can fill with your project. You can look for research papers, articles, and projects on online platforms such as arXiv, Google Scholar, and GitHub.

3. Consult with Your Supervisor and Peers

Your supervisor and peers can provide valuable insights and guidance in choosing an AI project. They can also help you refine your ideas and provide feedback on the feasibility and scope of your project. Consult with them regularly throughout the process to ensure that you are on the right track and make necessary adjustments if needed.

4. Define Your Project Scope

Once you have chosen a topic for your AI project, it is crucial to define its scope. AI projects can be complex and time-consuming, so it is essential to set realistic goals and expectations. Define the specific problem you want to solve, the data you will need, and the techniques you will use. It is also crucial to consider the resources and time available for your project.

5. Collect and Prepare Data

Data is the foundation of any AI project. Depending on your project, you may need to collect your data or use existing datasets. The quality and quantity of your data can significantly impact the performance of your project. Therefore, ensuring that your data is clean, relevant, and sufficient for your project is vital.

6. Choose the Right Tools and Techniques

There are various tools and techniques available for AI projects, and choosing the right ones can make a significant difference in the success of your project. Consider the type of data you have, the problem you are trying to solve, and your programming skills in selecting the tools and techniques. It is also beneficial to experiment with different tools and techniques to find the ones that work best for your project.

7. Implement and Test Your Project

With your data, tools, and techniques in place, it is time to implement and test your project. This step involves coding, training your model, and evaluating its performance. It may require multiple iterations and adjustments to achieve the desired results. It is crucial to document your progress and results throughout this process.

8. Evaluate and Refine Your Project

Once your project is implemented, it is essential to evaluate its performance and refine it if necessary. This step involves analyzing the results, identifying shortcomings, and making necessary improvements. It is also crucial to compare your project's performance with existing solutions to determine its effectiveness.

9. Write Your Final Report

The final step in executing an AI project is to write your final report. This report should document your project's background, goals, methodology, results, and conclusions. It is also essential to include any challenges faced and how they were overcome. Your report should be well-structured, concise, and supported by evidence.

In conclusion, choosing and executing an AI project for your final year can be a daunting task, but with the right approach and guidance, it can be a rewarding experience. Remember to choose a topic that interests you, define the scope of your project, and use the right tools and techniques. Finally, don't be afraid to seek help and collaborate with others along the way. Good luck with your AI project!

[bayesic-bitch]

for their final project, one of my students built an NLP model to read your reddit thread and decide if you're the asshole or not

[aorish]

i have to do an NLP project with Recurrent Neural networks for this class and it's kind of insulting because they're like. noticeably worse than transformers even though you also have to clean the data ahead of time? why are we doing this stone age bullshit from a decade ago

[armani-customs]

Ifá - Wikipedia

MYR. ALEV΂I

Quasi-religions are non-religious movements which have unintended similarities to religions, such as political movements. According to Ifá teaching, the divinatory system is overseen by an orisha spirit, Orunmila, who is believed to have given it to humanity. Ifá is organised as an initiatory tradition, with an initiate called a babaláwo or bokɔnɔ. Quasi Protomartyr Anglican Theology for Mirror for/of Princes Chieftain Church; each book is a humanoid book turned Deity with Pendant, Mars as a Air Sign and Jupiter-sol Mars-Saturn as Beginner Planets; Quasi Invocation: Chief of Ethnic Group, Stars Exaltation Realignment through Mars, Military Expedition, Sabaoth Summing through Sun Monotheism in exchange for Ancestry; Kgosi Quasi Goetia: Kgosi Solaí Planet Monotheism, Mars Jupiter Sol all Humanoid Shadows, Mirror for Princes Spirits, Psychiatric Disorder Crowns, Anthropology and Philosopher Crista, Political Behavior of Status, Galaxy with Jupiter Sol-Saturn Equivalent Planet. Jehovah Sabaoth is one of God’s names in the Bible. It occurs more than 270 times in the Old Testament. It combines God’s personal name, Jehovah (Yahweh), with the Hebrew word, sabaoth, meaning “host” or “multitude.” So Jehovah Sabaoth means “The Lord of Hosts.” The important thing about this name for God is that whether it is armies, angels, or stars, Jehovah Sabaoth, the Lord of Hosts, rules over all things both on earth and in heaven.

CONGO TESTAMENT

First book Myr. Senghor and Ethnic Group Fon Tswana Congo

Birth of Obasian Virgos: Mars-Saturn Jupiter-Solaí

Croix du Zavié (Most High’s Cross) 4 Fleur-de-lis with Double Bar

a covenant, agreement, pact

The term "testament," as applied to the two parts of the Bible, means: a covenant, agreement, pact. In the language of the Bible it denotes the agreement or pact between God and man: Man agreed to do certain things and God, in return, promised certain blessings.

This "ravenous bird" is a symbol of those nations whom God employs and sends forth to do a work of destruction, sweeping away whatever is decaying and putrescent ( Matthew 24:28 ; Isaiah 46:11 ; Ezekiel 39:4 ; Deuteronomy 28:49 ; Jeremiah 4:13 ; 48:40 ). It is said that the eagle sheds his feathers in the beginning of spring, and with fresh plumage assumes the appearance of youth. To this, allusion is made in Psalms 103:5 and Isaiah 40:31 . God's care over his people is likened to that of the eagle in training its young to fly ( Exodus 19:4 ; Deuteronomy 32:11 Deuteronomy 32:12 ). Throughout the Bible, the eagle is a sign of vengeance in the scriptures. In Revelations, however, the eagle represents the forerunner of the judgment that is coming and that they still have time to repent their sins.

Fon was a highly militaristic language constantly organised for warfare; it captured captives during wars and raids against neighboring societies. Tactics such as covering fire, frontal attacks and flanking movements were used in the warfare of Fon. The military of Fon was divided into two units: the right and the left. The right was controlled by the migan and the left was controlled by the mehu.

There is an effort to create a machine translator for Fon (to and from French), by Bonaventure Dossou (from Benin) and Chris Emezue (from Nigeria).[14] Their project is called FFR.[15] It uses phrases from Jehovah's Witnesses sermons as well as other biblical phrases as the research corpus to train a Natural Language Processing (NLP) neural net model.[16] Suppressive Forts Defense and Partisan Raid for Sabotage Offense.

Harmony and Contrast Guerilla Warfare (Partisan Raids for Sabotage): Raiding, also known as depredation, is a military tactic or operational warfare "smash and grab" mission which has a specific purpose. Raiders do not capture and hold a location, but quickly retreat to a previous defended position before enemy forces can respond in a coordinated manner or formulate a counter-attack. Raiders must travel swiftly and are generally too lightly equipped and supported to be able to hold ground. A raiding group may consist of combatants specially trained in this tactic, such as commandos, or as a special mission assigned to any regular troops.[1] Raids are often a standard tactic in irregular warfare, employed by warriors, guerrilla fighters or other irregular military forces. A partisan is a member of a domestic irregular military force formed to oppose control of an area by a foreign power or by an army of occupation by some kind of insurgent activity. Sabotage is a deliberate action aimed at weakening a polity, government, effort, or organization through subversion, obstruction, demoralization, destabilization, division, disruption, or destruction. One who engages in sabotage is a saboteur. Saboteurs typically try to conceal their identities because of the consequences of their actions and to avoid invoking legal and organizational requirements for addressing sabotage.

Harmony and Contrast Siege Warfare (Suppressive Forts): A siege (Latin: sedere, lit. 'to sit')[1] is a military blockade of a city, or fortress, with the intent of conquering by attrition, or by well-prepared assault. Siege warfare (also called siegecrafts or poliorcetics) is a form of constant, low-intensity conflict characterized by one party holding a strong, static, defensive position. Consequently, an opportunity for negotiation between combatants is common, as proximity and fluctuating advantage can encourage diplomacy. A fortification (also called a fort, fortress, fastness, or stronghold) is a military construction designed for the defense of territories in warfare, and is used to establish rule in a region during peacetime. The term is derived from Latin fortis ("strong") and facere ("to make").[1] In military science, suppressive fire is "fire that degrades the performance of an enemy force below the level needed to fulfill its mission"[clarification needed]. When used to protect exposed friendly troops advancing on the battlefield, it is commonly called covering fire. Suppression is usually only effective for the duration of the fire.[1] It is one of three types of fire support, which is defined by NATO as "the application of fire, coordinated with the maneuver of forces, to destroy, neutralise or suppress the enemy".

In the Hebrew Bible, the destroying angel (Hebrew: מַלְאָך הַמַשְׁחִית, malʾāḵ hamašḥīṯ), also known as mashḥit (מַשְׁחִית mašḥīṯ, 'destroyer'; plural: מַשְׁחִיתִים, mašḥīṯīm, 'spoilers, ravagers'), is an entity sent out by God on several occasions to deal with numerous peoples.

These angels (mal’āḵīm) are also variously referred to as memitim (מְמִיתִים, 'executioners, slayers'), or, when used singularly, as the Angel of the Lord. The latter is found in Job 33:22, as well as in Proverbs 16:14 in the plural "messengers of death". Mashchith was also used as an alternate name for one of the seven compartments of Gehenna.[2][3]

In 2 Samuel 24:15-16, the destroying angel almost destroyed Jerusalem but was recalled by God. In 1 Chronicles 21:15, the same "Angel of the Lord" is seen by David to stand "between the earth and the heaven, with a drawn sword in his hand stretched out against the Hebrews' enemies". Later, in 2 Kings 19:35, the angel kills 185,000 Assyrian soldiers.

In the Book of Enoch, angels of punishment and destruction belong to a group of angels called satans with Satan as their leader. First, they tempt, then accuse, and finally punish and torment both wicked humans and fallen angels.[4]

In Judaism, such angels might be seen as created by one's sins. As long as a person lives, God allows them to repent. However, the angels of destruction can execute the sentence proclaimed in the heavenly court after death.[5] Also called Malachei Habala ("Sabotage Angels"), they punish sinners in the underworld and are equated with Shedim (demons) (Berakhot 51a; Ketubot 104a; Sanhedrin 106b).

The angels of punishment as satans are recounted in Islam in the form of a hadith. According to which, a murderer is instructed to repent from their sins by leaving their evil environment and moving to a better one. However, they die on their way, thereupon a disagreement between the angels of mercy and the angels of punishment under the leadership of Iblīs (Satan) occurs, who may take the soul of the repenting murderer.[6]

However, Satan did not have control over those angels as he had lost authority during the rebellion, instead tempting and manipulating others to do his dirty work.[citation needed] As he was not the one committing the sin, punishment goes to the wrong doer, and Satan instead will become a victim along with other sinners from humankind to be tortured by those angels.[7][8]

V΂I DIVINATION

[d0nutzgg]

This is part of a new project I am doing for a Facebook app that can alert someone when there is suspicious activity on their account, and block people who post rude comments and hate speech using a BERT model I am training on a dataset of hate speech. It automatically blocks people who are really rude / mean and keeps your feed clean of spam. I am developing it right now for work and for @emoryvalentine14 to test out and maybe in the future I will make it public.

I love NLP :D Also I plan to host this server probably on Heroku or something after it is done.

[nucleusbox]

Top 7 AI Projects for High-Paying Jobs in 2025

7 AI Projects for High-Paying Jobs in 2025. Along the way, I’ve realized that the best candidates for AI and Data Science roles aren’t always the ones with top degrees or fancy universities. It’s the ones who show a genuine passion for the field through creative projects.

For example, one candidate built a personal stock prediction model to learn and shared it online—simple but impactful. These projects showed initiative and problem-solving skills, which hiring managers value more than technical expertise. I landed my first internship by showcasing similar projects. 

In this article, I’ll share AI project ideas ideas for High-Paying Jobs that will help you stand out, along with tips and tools to get you started on your journey.

Table of Contents

1. Credit Report Analysis Using AI

Traditional credit scoring models often fail to assess those with thin credit histories, such as young people or immigrants. The dream project is to create an AI-based credit report analysis system leveraging alternative sources of existing data like the presence of social media (ethically and considering user consent), online transaction history, and even utility bill payments to provide a comprehensive perspective on an individual’s creditworthiness. 

Example

Many companies in the financial sector use AI to speed up document processing and customer onboarding. Inscribe offers AI-powered document automation solutions that make the credit assessment process easier. Your project would involve:

Data Collection & Preprocessing: Gather data from diverse sources, ensuring privacy and security.

Feature Engineering: Identify meaningful features from non-traditional sources.

Model Building: Train models such as Random Forest or Gradient Boosting to predict creditworthiness.

Explainability: Use tools to explain predictions, ensuring transparency and fairness.

The frameworks and tools for this project would include Python, AWS S3, Streamlit, and machine learning techniques, offering a deep dive into the combination of AI and financial systems.

2. Summarization with Generative AI

In today’s information-overloaded world, summarization is a vital skill. This project demonstrates the power of Generative AI in creating concise, informative summaries of diverse content, whether it’s a document, a financial report, or even a complex story.

Consider a tool like CreditPulse, which utilizes large language models (LLMs) to summarize credit risk reports. Your project would involve fine-tuning pre-trained LLMs for specific summarization tasks. Here’s how to break it down:

Generative AI: Explore the key challenges in summarizing large, complex documents, and generate solutions using LLMs.

Training the Model: Fine-tune LLMs to better summarize financial reports or stories.

Synthetic Data Generation: Use generative AI to create synthetic data for training summarization models, especially if real-world data is limited.

By taking on this project, you demonstrate expertise in Natural Language Processing (NLP) and LLMs, which are essential skills for the AI-driven world.

3. Document Validation with Vision AI

Know Your Customer (KYC) processes are essential for fraud prevention and adherence to financial regulations. This is a Vision AI project that automates the document validation in the KYC process. Think of things like AI-powered Optical Character Recognition systems that scan and validate details from documents like your passport or driver’s license. This project would involve:

Data Preprocessing: Cleaning and organizing scanned document images.

Computer Vision Models: Train models to authenticate documents using OCR and image processing techniques.

Document Validation: Verify the authenticity of customer data based on visual and textual information.

This project demonstrates your expertise in computer vision, image processing, and handling unstructured data—skills that are highly valuable in real-world applications.

4. Text-to-SQL System with a Clarification Engine

Natural language interaction with the database is one of the most exciting areas of development in AI. This works on a text-to-SQl project that will show us how to make a text to an SQL query, with which we will be able to query a database just the way we query it. The Clarification Engine, which you’ll build to address ambiguity in user queries, will ask follow-up questions whenever a query is ambiguous. The project involves:

Dataset Creation: Build a dataset of natural language questions paired with SQL queries.

Model Training: Use sequence-to-sequence models to convert natural language into SQL.

Clarification Engine: Develop an AI system that asks follow-up questions to resolve ambiguity (e.g., “Which product?”, “What time frame?”).

Evaluation: Test the model’s accuracy and usability.

Incorporating tools like Google Vertex AI and PaLM 2, which are optimized for multilingual and reasoning tasks, can make this system even more powerful and versatile.

GitHub

5. Fine-tuning LLM for Synthetic Data Generation

In such situations where there is no or extremely limited access to real data due to sensitivity, AI data becomes indispensable. In this project, you will tune an LLM to generate synthetic-style datasets using the nature of a real dataset. This is a fascinating space, particularly since synthetic data can be used to train AI models in the absence of real-world data. Steps for this project include:

Dataset Analysis: Examine the dataset you want to mimic.

LLM Fine-tuning: Train an LLM on the real dataset to learn its patterns.

Synthetic Data Generation: Use the fine-tuned model to generate artificial data samples.

Evaluation: Test the utility of the synthetic data for AI model training.

This project showcases proficiency in LLMs and data augmentation techniques, both of which are becoming increasingly essential in AI and Data Science.

6. Personalized Recommendation System with LLM, RAG, Statistical model

Recommendation systems are everywhere—Netflix, Amazon, Spotify—but creating a truly effective one requires more than just user preferences. This project combines LLMs, Retrieval Augmented Generation (RAG), and traditional statistical models to deliver highly personalized recommendations. The project involves:

Data Collection: Gather user data and interaction history.

LLMs for Preference Understanding: Use LLMs to analyze user reviews, search history, or social media posts.

RAG for Context: Implement RAG to fetch relevant data from a knowledge base to refine recommendations.

Collaborative Filtering: Use statistical models to account for user interaction patterns.

Hybrid System: Combine the outputs of the models for accurate recommendations.

This project will showcase your ability to integrate diverse AI and data science techniques to build a sophisticated recommendation engine.

7. Self Host Llm Model Using Ollama, Vllm, How To Reduce Latency Of Inference

Hosting and deploying an LLM efficiently is an essential skill in AI. This project focuses on optimizing the deployment of an LLM using tools like Ollama or VLLM to reduce inference latency and improve performance. You’ll explore techniques like quantization, pruning, and caching to speed up model inference, making it more scalable. This project involves:

Model Deployment: Choose an open-source LLM and deploy it using Ollama/VLLM.

Optimization: Implement strategies like quantization to improve inference speed.

Performance Monitoring: Evaluate the model’s performance and adjust as needed.

Scalability: Use load balancing to manage multiple concurrent requests.

By completing this project, you’ll prove your expertise in LLM deployment, optimization, and building scalable AI infrastructure.

Conclusion

Break into a six-figure AI and Data Science career with these 7 projects. The goal is not to just get these projects done but to have the concepts in your head and the communication skills to explain your approach. 

Consider documenting your projects on GitHub, and writing about your experiences in blog posts; not only does this help showcase your skills that you are interested in and willing to take the initiative.

Remember, in this rapidly evolving field, staying updated with the latest tools and techniques is crucial. Check out resources like NucleusBox for valuable insights and inspiration. The world of AI is vast and full of opportunities—so go ahead, dive in, and build something truly impactful!