Introduction to the Machine Learning Project
The machine learning repository offers an expansive journey through the world of data science and machine learning, meticulously documented in Jupyter Notebooks. The project provides a learning pathway and a practical approach to understanding various concepts, implementation techniques, and advanced methodologies within the data science field. Through the use of Python's scientific stack and a suite of open-source libraries, the project focuses on providing clear, mathematical foundations accompanied by hands-on coding examples.
Deep Learning
Deep learning, a subset of machine learning, is explored extensively within this project. The documentation includes working from scratch with foundational concepts such as Softmax Regression, through to more complex architectures like Convolutional and Recurrent Neural Networks (CNNs and RNNs). Additionally, it extends to specialized techniques like Word2vec for natural language processing and modern architectures such as Transformers, exploring distinct applications like machine translation and question-answering models. Topics such as Graph Neural Networks and innovative methods like subword tokenization are also covered, catering to natural language and image processing tasks.
Model Deployment
The process of turning trained models into functional services is addressed under model deployment. It covers deploying machine learning models using FastAPI in conjunction with Azure Kubernetes Services, offering end-to-end guidance. Other topics include optimizing inferencing using gradient boosted trees and transformers as well as working with AWS services for data management tasks.
Operations Research
This section introduces operations research leveraging OR-Tools, providing an understanding of how data science can solve complex real-world optimization problems.
Reinforcement Learning
The concept of multi-armed bandits is introduced to provide a foundational understanding of reinforcement learning's principles and applications, setting the stage for more complex decision-making systems.
Advertisements and Auctions
For those venturing into digital advertising, the project presents a quick overview of auction models used for pricing ads, specifically elaborating on the Generalized Second Price Auction, which is pivotal in the digital marketing ecosystem.
Information Retrieval
The project explores search engine technology, with examples using ElasticSearch to implement algorithms like BM25, a crucial component of modern information retrieval systems.
Time Series
Forecasting methods are crucial for dealing with temporal data, and the project provides insights into exponential smoothing and adopting supervised-learning frameworks for time series forecasts. It also delves into signal processing using Fourier transformations.
Comprehensive Projects
Real-world projects such as the Rossman Store Sales prediction and Quora Insincere Questions classification are tackled from data preprocessing to model deployment. These projects enable users to encounter holistic machine learning workflows, tackling practical challenges.
A/B Testing
Understanding how to evaluate and analyze modifications in products or services is conducted through A/B testing methodologies. It includes discussions on statistical concepts important for conducting effective experiments.
Model Selection and Evaluation
Choosing the right model and evaluating its performance is crucial. Topics covered include cross-validation, metrics for imbalanced datasets, and advanced techniques like partial dependence plots. Methods to calibrate the probabilistic predictions of models are detailed to ensure the accuracy and reliability of predictions.
Dimensionality Reduction
Dimensionality reduction techniques such as Principal Component Analysis (PCA) are presented, which help in simplifying datasets and improving model performance by reducing the complexity.
This repository is continuously updated, reflecting the evolving nature of data science technology and methodologies. The overall aim of the project is to blend theoretical understanding with practical implementation, creating a comprehensive educational resource for anyone keen on mastering machine learning.