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  • $100 hourly
    I'm a data scientist and statistician with 3+ years of experience in tech. After working at Lucid Software for several years, I decided to go back to school to up-level my skills with a PhD in statistics at the University of Michigan. Some projects I've tackled in the past include: - Building customer lifetime value (CLV/LTV) models to save $1M+/yr - Forecasting account growth to optimize sales strategy on a team of over 200 sales reps - Leveraging time series models to set data-driven goals for customer success teams - Designing a (Bayesian) framework for analyzing hundreds of A/B tests - Building out critical pieces of data infrastructure, including a deployment of dbt for building database tables and version control for data science workloads I'm particularly strong in the following areas of data science: - Statistics modeling, especially Bayesian models - Causal inference: A/B testing, matching, propensity score weighting, randomization inference, experimental design - Time series forecasting - Custom algorithm design: I've implemented several projects 'from scratch' when the best method was not supported by standard libraries) - Theory/mathematics of probability and statistics - Data science soft skills: problem framing, project planning, communication, & data visualization I'm also proficient in these areas: - Artificial intelligence (AI) / machine learning (ML) / deep learning (DL) - Reinforcement learning (RL), especially contextual bandit algorithms I'm familiar with the following tools (but I'm open to learning others): - Deep learning frameworks: PyTorch, keras, tensorflow - SQL, especially Postgres and Snowflake - dbt - Tableau - Programming languages: Python, R, C++ - Probabilistic programming languages: Stan, PyMC3
    vsuc_fltilesrefresh_TrophyIcon Collaborative Filtering
    Scala
    Artificial Intelligence
    Machine Learning
    Statistics
    Bash
    Bayesian Statistics
    Apache Spark
    dbt
    SQL
    Python
    Tableau
    R
  • $35 hourly
    🏆 Google Certified TensorFlow Developer 🏆 AWS Certified Machine Learning - Specialty Engineer 🏆 AWS Certified Data Analytics - Specialty Engineer 5+ years of comprehensive industry experience in computer vision, Natural Language Processing (NLP), Predictive Modelling and forecasting. ➤ Generative AI Models 📍 OpenAI ( GPT - 3/4, ChatGPT, Embeddings ) 📍 GCP PaLM, Azure OpenAI Service 📍 Stable Diffusion - LoRA, DreamBooth 📍 Large Language Models (LLMs) - BLOOM, LLaMA, Llama2, Falcon ➤ Generative AI Frameworks 📍 LangChain 📍 Chainlit 📍 Pinecone - Vector database 📍 Langfuse ➤ ML Frameworks 📍 TensorFlow 📍 PyTorch 📍 Huggingface 📍 Keras 📍 Scikit-learn 📍 Spark ML 📍 NVIDIA DeepStream SDK Development ➤ DevOps 📍CI/CD 📍Git, Git Action 📍AWS - CodeCommit, CodeBuild, CodeDeploy, CodePipeline, CodeStar ➤ Cloud Skills 📍 AWS - SageMaker, Comprehend, Translate, Textract, Polly, Forecast, Personalize, Rekognition, Transcribe, IoT Core, IoT Greengrass 📍 GCP - Vertex AI, AutoML, Text-to-Speech, Speech-to-Text, Natural Language AI, Translation AI, Vision AI, Video AI, Document AI, Dialogflow, Contact Center AI, Timeseries Insights API, Recommendations AI 📍 Azure - Azure ML ➤ Sample work Applications include but are not limited to: 📍 Sales forecasting 📍 Recommendation engines 📍 Image classification 📍 Object segmentation 📍 Face recognition 📍 Object detection & object tracking 📍 Stable Diffusion Generative AI 📍 Augmented Reality 📍 Emotion analysis 📍 Video analytics and surveillance 📍 Text analysis and chatbot development 📍 Image caption generation 📍 Similar Image search engine 📍 Fine-tuning large language models (LLMs) 📍 ChatGPT API
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    Artificial Intelligence
    Amazon Redshift
    AWS Glue
    Google Cloud Platform
    Amazon Web Services
    Image Processing
    Python
    Amazon SageMaker
    Computer Vision
    TensorFlow
    Machine Learning
    Google AutoML
    PyTorch
    Natural Language Processing
    Deep Learning
  • $99 hourly
    ✅ EXPERT-VETTED 🎖 TOP RATED PLUS (Top 1% 🥇 of GLOBAL talent on Upwork). I am a Data Scientist, AI, Machine Learning, and Software Engineer with over 7 years of professional experience building software at any scale. I used to work at large companies such as Yandex as well as in many successful startups. Now I am running Shapeion Technologies, providing software and ML consulting for companies around the globe, to accomplish their business goals. I have been managing startup projects with client teams and have helped draft product roadmaps, planning, and talent acquisition. I have a good network of engineer talent who can help build product MVPs effectively. Products made by me helped startups to raise millions of USD. I have a team of 15 professional Data Annotation specialists who have completed many projects in the Natural Language Processing and Computer Vision fields. They are fluent in the following data annotation platforms: CVAT, Doccano, and ProdiGy. Here is a brief summary of my expertise: ● 2000+ worked hours and 30+ projects completed PERSONALLY only at Upwork ● Created MVPs for various startups and companies that raised millions of USD ● Helped 30+ businesses to reach their goals in time ● Taught the first Machine Learning private course in Belarus I am proficient in: ✅ Data Science and Machine Learning: ● classification and regression tasks ● time-series prediction (ARIMA, ...) ✅ Natural Language Processing (NLP): ● Text classification (including Sentiment Analysis, Aspect-Based Sentiment Analysis) ● Named Entity Recognition (NER, automatic extraction of relevant entities, like names, locations, dates, etc) ● Chat-Bots (Deep Learning or Rule-Based chat-bots for various domains) Frameworks: SpaCy, HuggingFace, Transformers, NLTK, Langchain, Pinecone Annotation platforms: Doccano, ProdiGy, Clarifai Architectures: BERT, LongFormer, GPT-3, GPT-4 Domains: MedTech, E-Commerce, HRM, AD-tech ✅ Computer Vision (CV): ● Image Classification ● Object Detection (YoLo) ● Instance segmentation / Semantic Segmentation (Mask R-CNN) ● Optical Character Recognition (OCR) Frameworks: PyTorch, TensorFlow, Keras, DarkNet, Detectron, Detectron2, Fast.ai Annotation platforms: CVAT, Clarifai Domains: Medical Imaging, CCTV (surveillance), NFT, LegalTech (document analysis) ✅ Full-Stack Development: ● Backend (FastAPI, Flask, Django, django-rest-framework, Node.js, JavaScript, PHP, Yii Framework) ● Frontend (Vue.js, React.js) ● WordPress ● CI/CD (Docker, docker-compose, Kubernetes, k8s, Ansible, Terraform) ● Databases (PostgreSQL, MySql, MongoDB, OracleDB, ArangoDB, ClickHouse, GraphDB) ● AMQ (Celery, RabbitMQ) Technical skills: Python, Javascript, Go, Java, Spring Boot, Gradle, Node.js, Angular, Tensorflow, Keras, NumPy, Serverless Frameworks, Docker, Terraform, Ansible, Kubernetes, Jenkins, Spinnaker, GitOps, DataOps, AWS. GCP, Azure, JAMstack, GraphQL, MEAN Stack, React, Redux, ES6, Express, MongoDB, Redis, Linux, Etcd, Consul, Kinesis, Redshift, DynamoDB, Spark, Hadoop, Kafka, PostgreSQL, MySQL, ELK, Sass, Webpack, Gulp, Git, Ethereum, Solidity, OpenZeppelin, Truffle, Flask, and Django. Thanks for your consideration! "Alexander was excellent. He managed expectations well and overperformed on the work. Communications were excellent. He saved us 100s of hours of work by automating our processes. He's more expensive than many people on Upwork, but appears to be exceptionally good an reliable." - Tom Mather
    vsuc_fltilesrefresh_TrophyIcon Collaborative Filtering
    Artificial Intelligence
    Database
    Data Science
    Machine Learning
    Chatbot
    Model Tuning
    Computer Vision
    Natural Language Processing
    PyTorch
    OpenCV
    Python
    Deep Learning
    Artificial Neural Network
  • $25 hourly
    As an ardent advocate at the forefront of Machine Learning and Data Science, I thrive on the leading edge of Generative AI 🧠 and optimization models. My journey in this dynamic arena is propelled by an unwavering commitment to innovation, shaping data products that transcend conventional boundaries and exceed business benchmarks. My greatest strength lies in the integration of automation 🤖, machine learning, and data science principles, meticulously tailored to address real-world business imperatives. I don't just deliver solutions; I devise game-changing strategies that catapult businesses ahead of their competitors 🚀. With my expertise, data becomes an indispensable asset 🔨, unlocking unparalleled opportunities for growth and achieving unprecedented success 🌟.
    vsuc_fltilesrefresh_TrophyIcon Collaborative Filtering
    Data Scraping
    API
    Data Science
    Machine Learning
    pandas
    Python Scikit-Learn
    Recommendation System
    Cluster Computing
    Gradient Boosting
    Decision Tree
    Bayesian Statistics
    Anomaly Detection
    NumPy
    Data Science Consultation
    Python
  • $30 hourly
    Proudly maintaining a 100% Job Success Score on Upwork and recognized among the Top 1% in fields such as Deep Learning, Machine Learning, and Computer Vision. Additionally, I rank among the Top 25 TensorFlow Freelancers worldwide. I hold a Bachelor's Degree in Computer Engineering. I have further honed my skills through specialized programs like the Advanced Data Analysis Nanodegree and a 6-month intensive Machine Learning Nanodegree from Udacity. My professional journey extends over two years, where I have developed expertise in Deep Learning, Data Analysis, and Natural Language Processing (NLP), working on diverse projects across these domains. My skill set includes: - Machine Learning & Deep Learning: Proficient in developing and refining algorithms tailored for practical applications. - Artificial Intelligence (AI): Skilled in crafting innovative AI solutions. - Natural Language Processing (NLP): Experienced in analyzing and interpreting human language to derive meaningful insights. - Data Analysis: Competent in dissecting complex data sets to identify trends and patterns. - Computer Vision: Specialized in processing and interpreting visual data. - Programming & Tools: Advanced proficiency in Python, adept at Web Scraping, and extensive experience with Keras and TensorFlow for deep learning projects. - Data Visualization: Expertise in presenting data in visually compelling formats. Dedicated to excellence, I approach each project intending to exceed expectations, blending technical knowledge with practical experience to achieve outstanding results.
    vsuc_fltilesrefresh_TrophyIcon Collaborative Filtering
    Problem Solving
    Artificial Intelligence
    Exploratory Data Analysis
    Data Modeling
    Deep Neural Network
    SQL
    Computer Vision
    Deep Learning
    Natural Language Processing
    Recommendation System
    Python
    Reinforcement Learning
    Anomaly Detection
    Data Science
    TensorFlow
  • $29 hourly
    With over 3 years of experience, I have specialized in helping retail traders with algorithmic trading, back-testing strategies, and using machine learning models to optimize their trading systems. - Algorithm Trading for Derivatives - Expertise in Options Trading - Trading Bot incorporated with ML models - Reinforcement Learning in Financial Market - Optimise Strategies Algorithm Trading: Python, Crypto, Trading Automation Bot, Price Forecasting/Analysis, ML model for Automated Trading, Market Making Bots, Indian Stock Broker API Integration - NSE & BSE (Zerodha, Upstox, Groww, AngelOne etc), Sports Betting, Automation for Derivatives Trading, Mechanical Trading System, API Integration, Algorithm Trading, Strategy Optimization with Back-Testing using Indicators and Price-Actions, Arbitrage Bot, Meta Trader 5 Integrated with Python, Bot integrated with TradingView Alerts(Webhooks), Copy Trading, Telegram Bot Integration for Daily Reports, Interactive Broker etc. Machine Learning: Supervised Learning, Unsupervised Learning, ML Predictive Modelling, Reinforcement Learning, AI enhanced Classification, Deep Learning, Recommendation System, Anomality Detection, Regression Models, LSTM, RNN, ANN, CNN, Cluster analysis, Feature extraction, Statistical classification, Artificial Intelligence, Feature engineering etc. Data Analytics: Web Scraping, Visualization with Seaborn, Web Automation, Pandas, Numpy, Streamlit, Plotly Dash, Time-Series Analysis and Forecasting etc.
    vsuc_fltilesrefresh_TrophyIcon Collaborative Filtering
    Qualitative Research
    Quantitative Analysis
    API Integration
    AI-Enhanced Classification
    Recommendation System
    TensorFlow
    Reinforcement Learning
    Unsupervised Learning
    Supervised Learning
    Algorithm Development
    Cryptocurrency
    Bot Development
    Machine Learning
    Trading Automation
    Python
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Collaborative Filtering FAQs

What is collaborative filtering?

When designing a recommendation system, there are two major ways to go about it. We’ve already talked about content-based filtering, but what do you do when your content is simply too massive or diverse to manually apply attributes? For that, there’s collaborative filtering, a technique that’s widely used across social media, retail, and streaming services. In this article, we’ll explore how collaborative filtering works, where it’s used, and what skills you might need to get started.

For all the sophisticated math and machine learning techniques involved, the concept behind collaborative filtering is pretty straightforward: It’s based on the idea that people who share an interest in certain things will probably have similar tastes in other things as well. You experience collaborative filtering first-hand every time you go online and see “Customers Who Bought This Item Also Bought,” or “Users like you also liked…”

Why collaborative filtering?

The main difference between collaborative filtering and content-based filtering is conceptual. Where content-based filtering is built around the attributes of a given object, collaborative filtering relies on the behavior of users. This approach has some distinct advantages over content-based filtering:

  • It benefits from large user bases. Simply put, the more people are using the service, the better your recommendations will become, without doing additional development work or relying on subject area expertise.
  • It’s flexible across different domains. Collaborative filtering approaches are well suited to highly diverse sets of items. Where content-based filters rely on metadata, collaborative filtering is based on real-life activity, allowing it to make connections between seemingly disparate items (like say, an outboard motor and a fishing rod) that nonetheless might be relevant to some set of users (in this case, people who like to fish).
  • It produces more serendipitous recommendations. When it comes to recommendations, accuracy isn’t always the highest priority. Content-based filtering approaches tend to show users items that are very similar to items they’ve already liked, which can lead to filter bubble problems. By contrast, most users have interests that span different subsets, which in theory can result in more diverse (and interesting) recommendations.
  • It can capture more nuance around items. Even a highly detailed content-based filtering system will only capture some of the features of a given item. By relying on actual human experience, collaborative filtering can sometimes recommend items that have a greater affinity with one another than a strict comparison of their attributes would suggest.

Two methods: user-item vs item-item

There are two approaches to collaborative filtering, one based on items, the other on users. Item-item collaborative filtering was originally developed by Amazon and draws inferences about the relationship between different items based on which items are purchased together. The more often two items (say, peanut butter and jelly) appear in the same shopping cart or user history, the “closer” they’re said to be to one another. So, when someone comes and adds peanut butter to their cart, the algorithm will suggest things that are close, like jelly or white bread, over things that aren’t, like motor oil.

User-item filtering takes a slightly different approach. Here, rather than calculating the distance between items, we calculate the distance between users based on their ratings (or likes, or whatever metric applies). When coming up with recommendations for a particular user, we then look at the users that are closest to them and then suggest items those users also liked but that our user hasn’t interacted with yet. So, if you’ve watched and liked a certain number of videos on Facebook, Facebook can look at other users who liked those same videos and recommend one that they also liked but which you might not have seen yet.

The important point here is that in both the examples above, the system has no idea why any of these items are related to one another, it only knows that they either show up in the same basket together or that they’re liked by people with similar preferences. In some cases, though, this can be a feature rather than a shortcoming, especially in cases where the items to be filtered are extremely heterogeneous, as in online retailers or social networks. (Note: This can also lead to some unanticipated situations, as when Amazon’s algorithm began unintentionally suggesting drug paraphernalia to users who bought a particular scale.)

How to calculate similarity?

The above descriptions are meant to be general overviews of how collaborative filtering techniques are typically implemented. Behind each implementation, there are a number of different techniques for measuring the similarity of two different items or users. Which one is right for a given recommender system depends on both the use case and the nature of the data involved.

When the data you’re working with is dense, a simple Euclidean distance measure can work. In reality, though, data (especially ratings) is often sparse. In these cases, cosine similarity is often used. There are other measures (Pearson correlation coefficient, k-nearest-neighbors, etc.). Fortunately, most of these functions are easily performed in Python (assuming you have the SciPy and scikit-learn libraries). Are you looking for a data scientist to build a recommendation engine? Hire a data scientist on Upwork today.

Challenges of collaborative filtering

  • Complexity and expense. Collaborative filtering algorithms can run into scalability problems when the number of users and items gets too high (think in tens of millions of users and hundreds of thousands of items), especially when recommendations need to be generated in real-time online. Potential solution: This is where distributed clusters of machines running Hadoop or Spark come in handy. Depending on your project, it may also be possible to calculate relationships offline overnight by way of batch processing, which makes serving recommendations much quicker even if they’re no longer being updated in real-time.
  • Data sparsity. Many user signals are ambiguous. Just watching a video doesn’t tell YouTube whether you liked that particular video or not, and just eating at a restaurant doesn’t tell Yelp whether you liked it or not. That’s why ratings are so important in collaborative-filtering systems. But users don’t rate every item they interact with, and many users don’t rate anything at all. Potential solution: Depending on the nature of the data, there may be proxy measures that can be used. Another common technique is to assume that missing reviews are equivalent to average reviews, though this is a very strong assumption in most cases.
  • The “cold start” problem. As we’ve seen, collaborative-filtering can be a powerful way of recommending items based on user history, but what if there is no user history? This is called the “cold start” problem, and it can apply both to new items and to new users. Items with lots of history get recommended a lot, while those without never make it into the recommendation engine, resulting in a positive feedback loop. At the same time, new users have no history and thus the system doesn’t have any good recommendations. Potential solution: Onboarding processes can learn basic info to jump-start user preferences, importing social network contacts.

Relevant skills and tech

Building a recommender system with collaborative filtering is a major project that involves both data science and engineering challenges. Solving these challenges may require expertise with data processing and storage frameworks like Hadoop or Spark. When it comes to implementing algorithms, data-oriented programming languages like Python, Java, and Scala support libraries that make it easy to perform an array of machine learning and statistical analysis tasks.

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