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Free Download E-Commerce Product Recommendation RAG Systems Published 10/2024 Created by Ahmad Varasteh MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 7 Lectures ( 38m ) | Size: 278 MB Get hands-on with a practical Generative AI course. What you'll learn Use OpenAI APIs to perform context-based searches efficiently and effectively. Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models. Implement cosine similarity to enhance recommendation systems by identifying and understanding data relationships and patterns. Craft context-reach prompt for a user-friendly product recommendation system Requirements Basic understanding of data analysis concepts Fundamental understanding of Python programming for data analysis and API interaction Access to OpenAI API Key Description By the end of this project, you will be equipped to perform context-based searches using Retrieval-Augmented Generation (RAG) systems and the OpenAI API, as well as develop a personalized recommendation system. You've been hired by ShopVista, a leading e-commerce platform offering products ranging from electronics to home goods. Your goal is to improve the platform's product recommendation system by creating a context-driven search feature that delivers tailored suggestions based on users' search phrases. You'll work with a dataset of product titles, descriptions, and identifiers to build a recommendation system that enhances the shopping experience.Learning Objectives:Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models.Implement cosine similarity to identify and understand data relationships and patterns, improving recommendation systems.Utilize OpenAI APIs to perform efficient and effective context-based searches.Design and develop context-rich prompts for a user-friendly product recommendation system.This project will provide you with a comprehensive understanding of AI-powered search and recommendation systems, enabling you to grasp how cutting-edge technologies such as Retrieval-Augmented Generation (RAG) and OpenAI's models can be applied to solve real-world challenges. As you work through the project, you'll learn how to prepare and manage large datasets, leverage advanced text embedding techniques, and use AI to improve user interactions with e-commerce platforms.By implementing context-based searches and personalized recommendation features, you'll enhance your technical capabilities in areas such as natural language processing, vector-based data retrieval, and algorithm development. Furthermore, the practical experience gained from building a recommendation system for a leading e-commerce platform like ShopVista will deepen your problem-solving skills, allowing you to address complex customer needs with AI-driven solutions. This hands-on experience will not only strengthen your expertise in the e-commerce domain but also broaden your ability to design user-centric applications that deliver personalized, relevant, and intuitive experiences. Who this course is for Data scientists who are looking for more hands-on practice with RAG systems and Generative AI. Homepage https://www.udemy.com/course/e-commerce-product-recommendation-rag-systems/ Screenshot Rapidgator https://rg.to/file/d1634c304c58cb20a8d34959a1250a6f/mujpl.ECommerce.Product.Recommendation..RAG.Systems.rar.html Fikper Free Download https://fikper.com/WqbwFNnuSA/mujpl.ECommerce.Product.Recommendation..RAG.Systems.rar.html No Password - Links are Interchangeable

Free Download Automating Ml Pipelines For Song Recommendation System Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.74 GB | Duration: 4h 46m Automate Song Recommendations with Docker, MLFlow, and CI/CD Practices for Machine Learning Algorithms. What you'll learn Understand the Math Behind ML Algorithms: You will learn the mathematical concepts that underlie popular machine learning algorithms. Implement Machine Learning Algorithms: You will gain hands-on experience in coding and applying various machine learning algorithms. Design and Build MLFlow Tracking: You will learn how to use MLFlow for tracking and managing machine learning experiments effectively. Implement Microservices with Docker: You will learn how to create and manage microservices for automating machine learning pipelines using Docker. Automate Model Training and Evaluation: You will learn to use Airflow triggers to automate the process of training and evaluating machine learning models. Set Up Git CI/CD for a Song Recommender App: You will learn how to implement CI/CD for a song recommendation web app. Requirements Basic Knowledge of Python programming, as it will be used for implementing machine learning algorithms and building ML pipeline microservices. A desire to learn and experiment with machine learning and microservices is encouraged. Description Math Behind Machine Learning Algorithms:K-Nearest Neighbors (KNN): A method for finding similar songs based on user preferences.Random Forest (RF): An algorithm that combines many decision trees for better predictions.Prin[beeep]l Component Analysis (PCA): A technique for reducing the number of features while retaining important information.K-Means Clustering: A way to group similar songs together based on features.Collaborative Filtering: Making recommendations based on user interactions and preferences.Data Processing Techniques:Feature Engineering (Feature Importance using Random Forest): Feature importance analysis and creating new features from existing data to improve model accuracy.Data Pre-processing (Missing Data Imputation): Cleaning and preparing data for analysis.Evaluation and Tuning:Hyperparameter Tuning (Collaborative Filtering, KNN, Naive Bayes Classifier): Adjusting the settings of algorithms to improve performance.Evaluation Metrics (Precision, Recall, ROC, Accuracy, MSE): Methods to measure how well the model performs.Data Science Fundamentals:TF-IDF (Term Frequency and Inverse Document Frequency): A technique for analyzing the importance of words in song lyrics.Correlation Analysis: Understanding how different features relate to each other.T-Test: A statistical method for comparing groups of data.Automation Tools:Building Microservices using Docker: Use containers to run applications consistently across different environments.Airflow: Automate workflows and schedule tasks for running ML models.MLFlow: Manage and track machine learning experiments and models effectively.By the end of the course, you will know how to build and automate the training, evaluation, and deployment of an ML model for a song recommendation system using these tools, libraries and techniques. Overview Section 1: Introduction Lecture 1 Course Introduction Section 2: Machine Learning - Math Intuition Lecture 2 Math Behind Collaborative Filtering Lecture 3 Math Behind KNN (Euclidean Distance) Lecture 4 Math Behind Naive Bayes (Bayes Theorem) Lecture 5 Math Behind TF and IDF Lecture 6 Math Behind Cosine Similarity Lecture 7 Evaluation Metric - MSE Lecture 8 Math Behind - K-Means Clustering (Unsupervised Learning) Lecture 9 Math Behind Prin[beeep]l Component Analysis Lecture 10 Math Behind Pearson Correlation Lecture 11 Math Behind - T-Statistic Test Lecture 12 Evaluation Metrics - Classification Models Section 3: ML Experimentation - Supervised & Unsupervised Learning Lecture 13 Module Artifacts Lecture 14 Project Env Setup (Conda) Lecture 15 Import required libraries Lecture 16 Understanding the features in data Lecture 17 Data Preprocessing Lecture 18 Feature Engineering Lecture 19 Pearson Correlation Analysis Lecture 20 T-Test Statistics Lecture 21 Collaborative Filtering - User Genre Matrix Lecture 22 Creation of user similarity network visualization (Cosine Similarity) Lecture 23 Songs Recommender Engine Model - Collaborative Filtering Lecture 24 Fetch Songs Recommendation - Collaborative Filtering Model Lecture 25 KNN and Naive Bayes Model Pipeline Lecture 26 Model Hyperparameter Tuning Lecture 27 Best Estimator Recommendation Lecture 28 K-Means Clustering and PCA Section 4: Airflow - Automate Collaborative Filtering model training and deployment Lecture 29 Module Artifacts Lecture 30 Code Environment Setup Lecture 31 MLFlow Lifecycle and Commands Lecture 32 Airflow Lifecycle and Commands Lecture 33 DAG Setup - Data Splitting, User Genre Matrix Generation, Training & Evaluation Lecture 34 train_and_deploy.py W/O Airflow Lecture 35 Optional - DAG Assets Validation Section 5: Building Microservices for MLFlow and Airflow using Docker Lecture 36 docker-compose.yml Lifecycle (Theory) Lecture 37 Dockerfile (Python and Airflow) Lecture 38 Microservices - docker-compose.yml Lecture 39 Building Docker Image for Python Lecture 40 Building Docker Image for Airflow Section 6: ML Pipeline Orchestration - Airflow Triggers and MLFlow Experiments Lecture 41 Build and Compose up the Microservices Lecture 42 Orchestrating the ML Job Triggers and Logs Section 7: Song Recommender System Web App Lecture 43 Import required modules Lecture 44 Load Pkl Model Lecture 45 Fallback condition for recommender system Lecture 46 Load and Fetch cache Data Lecture 47 Building UI for song recommender system Lecture 48 Filter and Join recommendations Lecture 49 Testing the recommender app in localhost environment Lecture 50 Push the codebase to Github repository Lecture 51 Deploy recommender app to Streamlit cloud with Github CI/CD Section 8: Challenges / Takeaways / Homework Lecture 52 Automating ML Pipeline Song Recommendation: Challenges / Takeaways / Homework Lecture 53 Thank you! Lecture 54 Codebase Artifacts Students pursuing studies in data science, computer science, or related disciplines who want to enhance their practical skills in machine learning and automation.,Individuals looking to deepen their understanding of machine learning and its applications in real-world scenarios, particularly in recommendation systems.,Programmers interested in expanding their skill set to include machine learning concepts and automation practices using tools like Docker, MLFlow, and Airflow.,Professionals wanting to learn how to build and automate machine learning pipelines and improve their workflow efficiency.,Anyone with a foundational knowledge of machine learning who wants to gain practical experience in implementing algorithms and automating processes.,Individuals looking to enhance their qualifications and job prospects by adding machine learning and automation expertise to their portfolio. 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