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Automating ML Pipelines for Song Recommendation System
Rating: 3.8 out of 5(2 ratings)
39 students

Automating ML Pipelines for Song Recommendation System

Automate Song Recommendations with Docker, MLFlow, and CI/CD Practices for Machine Learning Algorithms.
Created byRavinthiran Partheepan
Last updated 10/2024
English

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.

Course content

8 sections • 54 lectures • 4h 47m total length

  • Course Introduction2:03

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.

  • Principal 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.

Who this course is for:

  • 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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