Machine Learning Tutorial Python - Machine Learning for Beginners
Comprehensive tutorial covering machine learning basics, algorithms, and projects using Python libraries. Includes deep learning, neural networks, and advanced topics for beginners to intermediate learners.
Amazon SageMaker Technical Deep Dive Series
Comprehensive deep dive into Amazon SageMaker, covering notebook instances, algorithms, custom models, training, deployment, tuning, and advanced features for efficient machine learning workflows.
Finding the Tallest Tree - Comparing Tree-Based Models
Explore various tree-based models in R, comparing decision trees, random forests, and boosted trees. Learn about different packages, their implementation, and evaluate models based on user-friendliness, accuracy, and speed.
TensorFlow Extended - Machine Learning Pipelines and Model Understanding
Explore TFX for building production-ready ML pipelines. Learn data processing, model analysis, and versioning techniques for large datasets, enhancing your machine learning workflow.
How Data Science Works
Explore the fundamentals of data science, from data acquisition to machine learning, with practical insights and best practices for effective analysis and decision-making.
How Data Science Works
Explore the fundamentals of data science through plain English explanations, covering key concepts like feature engineering, machine learning, and deep learning without technical jargon.
End-to-End Python Machine Learning Project - Real Estate Price Prediction
Comprehensive Hindi tutorial on building a real estate price prediction model using Python. Covers data analysis, preprocessing, model creation, and evaluation for practical machine learning application.
Neo4J GDS: Machine Learning Feature Engineering in Spanish
Aprende a realizar ingeniería de características y preprocesamiento de datos con Neo4J y la librería GDS para ciencia de datos y aprendizaje automático.
Rapids Machine Learning Training y Feature Engineering en tu GPU con CuML y CuDF
Acelera el entrenamiento de modelos y la ingeniería de características utilizando GPU con CuML y CuDF para optimizar tus flujos de trabajo de aprendizaje automático.
Analysis and Application of New Features in Flink ML 2.2.0
Explore Flink ML 2.2.0's new features, including online inference, feature engineering, and real-time log clustering applications in Alibaba's infrastructure.
Введение в машинное обучение - Часть V
Изучите основы машинного обучения, включая ключевые концепции и методы, для развития навыков в этой перспективной области информационных технологий.
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Explores deep learning models for malware classification, analyzing learned features from byte-level to end-to-end, connecting activations to binary semantics, and examining impacts of training data and regularization.
Identifying Ubiquitous Third Party Libraries in Compiled Executables Using Annotated and Translated
Explore techniques for identifying third-party libraries in compiled executables, focusing on firmware indicator translation and machine learning approaches for enhanced security analysis.
Detecting Website Intrusion and Account Compromise with Machine Learning
Explore machine learning techniques for detecting website intrusions and account compromises, covering data analysis, feature engineering, and real-time integration in open environments.
How a Data Scientist Fights Fraud - With Code!
Explore data science techniques for fraud detection, including feature engineering, SQL querying, model creation, evaluation, and interpretation, with hands-on coding examples.
How to Solve Santander Kaggle Transaction Competition - Top 1% Solution, No Ensemble
Learn to achieve top 1% in Santander Kaggle competition using neural networks, feature engineering, and data analysis techniques. Improve your machine learning skills with practical insights and strategies.
Data Science Solutions with Cortana Analytics
Comprehensive tutorial on building predictive models using Microsoft Azure Machine Learning, covering data processing, model creation, deployment, and practical applications in various domains.
Kaggle Competition Starter Notebook Walkthrough
Learn to create a starter notebook for a Kaggle competition, covering feature engineering, cross-validation, and LightGBM modeling. Gain practical insights from a Kaggle Grandmaster on predictive modeling and Python coding.
Introduction to Python for Data Science
Explore Python tools for data science, covering data processing, visualization, and modeling. Learn end-to-end data science through a real-world case study with expert insights.
Scaling Machine Learning Feature Engineering in Apache Spark at Facebook
Explore advanced techniques for scaling ML feature engineering in Apache Spark, including Feature Injection and Feature Reaping, with optimizations for improved performance and efficiency at Facebook's scale.
Scaling XGBoost for Thousands of Features with Databricks
Scaling XGBoost for online advertising: Learn to handle thousands of features, optimize model training, and avoid common pitfalls in both Python and Scala implementations on Spark.