Entity Resolution at Scale
Explore entity resolution techniques for cleaning large-scale data, focusing on challenges and solutions using Apache Spark to process billions of records efficiently.
Do You Live or Die - Explaining Machine Learning with Azure and the Titanic Dataset
Explore machine learning using Azure and the Titanic dataset, covering data preparation, model building, and evaluation for predictive analysis and security applications.
Data, Design, Code
Explore the process of creating web-based data visualizations, from topic selection and data preparation to designing visual encodings, with insights on using D3.js for custom, impactful visualizations.
Record Deduplication with Python
Aprenda técnicas de deduplicação de registros em Python para identificar duplicatas em conjuntos de dados sem identificadores únicos, aplicável em governos e empresas.
Skynet 101 - How to Keep Your Machine Learning Code From Getting Away From You
Discover how to apply Test-Driven Development to machine learning projects, from data analysis to building recommendation engines, using Python libraries and Jupyter Notebooks.
Everyone Can Do Data Science in Python
Learn essential data science techniques using Python, numpy, pandas, and sklearn. Explore real-world use cases with code snippets and live demos in IPython notebooks.
Good Features Beat Algorithms
Explore feature engineering and selection techniques to improve machine learning model accuracy. Learn how to develop an ensemble for measuring feature importance in real-world datasets.
Top 15 Python Tips for Data Cleaning - Understanding
Discover 15 essential Python tips for effective data cleaning and understanding, focusing on Pandas and NumPy libraries to enhance your data analysis skills.
I Have to Confess, I Still Love Pandas
Discover the power of Pandas for data science workflows. Learn advantages, efficient usage techniques, and fundamental concepts to enhance your data manipulation skills.
Python, Visual Studio Code, Copilot - Revolutionizing the Way You Do Data Science
Explore how Visual Studio Code, GitHub, Codespaces, and Copilot enhance data science workflows, boosting productivity through advanced tools like VS Code Notebooks, Data Wrangler, and AI-assisted coding.
Code Like a Snake Charmer – Advanced Data Modeling in Python
Comprehensive Python data modeling tutorial covering extraction, cleaning, transformation, visualization, predictive modeling, validation, and deployment using popular libraries and Azure integration.
Data Munging to Wrangling - 7 Steps to Mastering Data Preparation for Data Science
Discover essential techniques for data preparation in AI/ML projects. Learn to source, clean, and transform data effectively, enhancing the quality and predictive power of machine learning models.
Kotlin for Machine Learning
Explore Kotlin's potential in machine learning projects, from data cleaning to model deployment. Learn about Kotlin tools for data science, including Jupyter notebooks, visualizations, and dataframes.
Machine Learning with Scala on Spark
Explore machine learning with Spark and Scala, comparing frameworks, discussing advantages, and addressing challenges in data science. Gain insights into Spark.ml's capabilities and potential for predictive modeling.
Building Billion Node Graphs for Machine Learning
Explore building billion-node graphs for machine learning, covering data ingestion, graph management, and algorithm execution at scale. Learn about challenges and solutions in handling massive datasets for GraphML research.
Using EEG and Azure Machine Learning to Perform Lie Detection
Explore EEG-based lie detection using Azure Machine Learning. Learn about data collection, model building, and result analysis, with insights on limitations and future directions.
Using EEG and Machine Learning to Perform Lie Detection
Explore lie detection using EEG and machine learning. Learn about data collection, Azure ML model building, and result analysis. Gain insights into brain-computer interfaces and their potential applications.
Data Cleansing With SQL And R
Learn data cleansing techniques using SQL and R to prepare and correct data efficiently, focusing on tidy data concepts and practical tools for simplifying research and analysis.
Not a Magic - What to Expect from Machine Learning Projects
Practical insights on implementing machine learning projects, covering data handling, algorithm selection, training, evaluation, and deployment challenges in organizational settings.
GenAI and Datacomp: Creating the Largest Public Multimodal Dataset in Academia
Explore academia's role in GenAI, focusing on large-scale dataset management. Learn about AI-driven data cleaning, data-centric AI, legal challenges, and synthetic dataset potential in this insightful talk.
Big Data is Dead - The End of an Era and the Future of Data Analytics
Explore why the Big Data era is ending and learn to focus on data utilization for better decision-making, drawing insights from industry analysis and expert experience.