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Practical Machine Learning with scikit-learn in Python

Build, train, and evaluate models. Explore talks on classification, regression, and clustering algorithms, plus techniques for data preprocessing and creating robust ML pipelines.

Matching moments

Q&A: Advantages of scikit-learn over other libraries
01:45 MIN

Q&A: Advantages of scikit-learn over other libraries

Overview of Machine Learning in Python

Q&A: Using scikit-learn for model evaluation
02:52 MIN

Q&A: Using scikit-learn for model evaluation

Overview of Machine Learning in Python

Building a model to predict CO2 emissions
06:32 MIN

Building a model to predict CO2 emissions

Getting Started with Machine Learning

Code demo: Preprocessing and training a classifier
06:47 MIN

Code demo: Preprocessing and training a classifier

Overview of Machine Learning in Python

Splitting data and selecting a model algorithm
04:27 MIN

Splitting data and selecting a model algorithm

Machine learning 101: Where to begin?

The production architecture and technology stack for AML AI
03:27 MIN

The production architecture and technology stack for AML AI

Detecting Money Laundering with AI

Using high-level frameworks like RAPIDS for data science
03:14 MIN

Using high-level frameworks like RAPIDS for data science

Accelerating Python on GPUs

Using high-level frameworks like Rapids for acceleration
02:56 MIN

Using high-level frameworks like Rapids for acceleration

Accelerating Python on GPUs

Comparing methods for machine learning with databases
02:59 MIN

Comparing methods for machine learning with databases

Using WebAssembly for in-database Machine Learning

Implementing pipelines with the getML Python API
00:45 MIN

Implementing pipelines with the getML Python API

What is relational learning and why does it matter?

How Python became the dominant language for AI
04:09 MIN

How Python became the dominant language for AI

AI in the Open and in Browsers - Tarek Ziadé

Exploring bag-of-words methods for text vectorization
05:42 MIN

Exploring bag-of-words methods for text vectorization

A beginner’s guide to modern natural language processing

Pre-built algorithms and utilities available in Spark MLlib
03:05 MIN

Pre-built algorithms and utilities available in Spark MLlib

PySpark - Combining Machine Learning & Big Data

Applying uncertainty for active learning and corner case detection
13:54 MIN

Applying uncertainty for active learning and corner case detection

Uncertainty Estimation of Neural Networks

Using supervised learning to find the best features
01:24 MIN

Using supervised learning to find the best features

What is relational learning and why does it matter?

Training AI models with custom scraped data
04:04 MIN

Training AI models with custom scraped data

Scrape, Train, Predict: The Lifecycle of Data for AI Applications

Using machine learning to classify touristic data
04:16 MIN

Using machine learning to classify touristic data

100 million days in Vienna: A story of APIs & AI in tourism.

Ranking Amazon reviews by learning a quality score
06:40 MIN

Ranking Amazon reviews by learning a quality score

Ranking Amazon Reviews by Quality with Pointwise Ratings learned from Pairwise Data

Other common machine learning models used in retail
08:24 MIN

Other common machine learning models used in retail

Data Science in Retail

Exploring the key features of the Spark MLlib library
04:33 MIN

Exploring the key features of the Spark MLlib library

PySpark - Combining Machine Learning & Big Data

Overview of methods for intelligent data selection
03:18 MIN

Overview of methods for intelligent data selection

Intelligent Data Selection for Continual Learning of AI Functions

Crowdsourcing and labeling training data for the model
08:46 MIN

Crowdsourcing and labeling training data for the model

Machine Learning for Software Developers (and Knitters)

Training a model and interpreting its predictions
09:38 MIN

Training a model and interpreting its predictions

AI is an Electric Bike for the Brain - Stoyan Stefanov

Using probabilistic programming for incomplete data
06:36 MIN

Using probabilistic programming for incomplete data

Augmented Intelligence for transport planning: Human in the Loop Modelling

Choosing gradient boosted trees for tabular data
01:56 MIN

Choosing gradient boosted trees for tabular data

Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing

How INSTINCT software identifies valuable data
02:50 MIN

How INSTINCT software identifies valuable data

Finding the unknown unknowns: intelligent data collection for autonomous driving development

The challenge of industrializing data science models
02:07 MIN

The challenge of industrializing data science models

Industrializing your Data Science capabilities

A three-step approach using LightGBM and SHAP
09:03 MIN

A three-step approach using LightGBM and SHAP

Solving the puzzle: Leveraging machine learning for effective root cause analysis

Experimenting with different machine learning model approaches
06:24 MIN

Experimenting with different machine learning model approaches

Shoot for the moon - machine learning for automated online ad detection

Using precision and recall for model training
03:05 MIN

Using precision and recall for model training

Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing

The standard workflow for machine learning in PySpark
04:21 MIN

The standard workflow for machine learning in PySpark

PySpark - Combining Machine Learning & Big Data

How InstructLab simplifies data generation for tuning
03:01 MIN

How InstructLab simplifies data generation for tuning

Unlocking the Power of AI: Accessible Language Model Tuning for All

Core concepts of machine learning models
02:20 MIN

Core concepts of machine learning models

Getting Started with Machine Learning

Using entropy minimization and pseudo-labeling techniques
02:22 MIN

Using entropy minimization and pseudo-labeling techniques

Semi-Supervised Learning. How to overcome the lack of labels

A four-step process for unsupervised outlier detection
04:33 MIN

A four-step process for unsupervised outlier detection

Detecting Money Laundering with AI

Applying supervised machine learning to pipeline data
04:16 MIN

Applying supervised machine learning to pipeline data

Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner

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Overview of Machine Learning in Python
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Overview of Machine Learning in Python

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Machine learning 101: Where to begin?
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Machine learning 101: Where to begin?

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Getting Started with Machine Learning
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Detecting Money Laundering with AI
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Detecting Money Laundering with AI

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Accelerating Python on GPUs
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Accelerating Python on GPUs
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Intelligent Data Selection for Continual Learning of AI Functions
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Intelligent Data Selection for Continual Learning of AI Functions

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Data Science in Retail
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Data Science in Retail

Julian Joseph

Anomaly Detection - Using unsupervised Machine Learning for detecting anomalies in customer base
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Anomaly Detection - Using unsupervised Machine Learning for detecting anomalies in customer base

Lukas Kölbl

Ranking Amazon Reviews by Quality with Pointwise Ratings learned from Pairwise Data
47:26

Ranking Amazon Reviews by Quality with Pointwise Ratings learned from Pairwise Data

Tanmay Bakshi

Machine learning in the browser with TensorFlowjs
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Machine learning in the browser with TensorFlowjs

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distil labs – small model training, made simple
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distil labs – small model training, made simple

Selim Nowicki

Finding the unknown unknowns: intelligent data collection for autonomous driving development
19:24

Finding the unknown unknowns: intelligent data collection for autonomous driving development

Liang Yu

Semi-Supervised Learning. How to overcome the lack of labels
27:03

Semi-Supervised Learning. How to overcome the lack of labels

Alex Timashov

PySpark - Combining Machine Learning & Big Data
43:26

PySpark - Combining Machine Learning & Big Data

Ayon Roy

Machine Learning for Software Developers (and Knitters)
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Machine Learning for Software Developers (and Knitters)

Kris Howard

Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing
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Lina Weichbrodt

Uncertainty Estimation of Neural Networks
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Uncertainty Estimation of Neural Networks

Tillman Radmer & Fabian Hüger & Nico Schmidt

What is relational learning and why does it matter?
28:00

What is relational learning and why does it matter?

Alexander Uhlig

The pitfalls of Deep Learning - When Neural Networks are not the solution
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The pitfalls of Deep Learning - When Neural Networks are not the solution

Adrian Spataru & Bohdan Andrusyak

Harry Potter and the Elastic Semantic Search
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Harry Potter and the Elastic Semantic Search

Iulia Feroli

Unlocking the Power of AI: Accessible Language Model Tuning for All
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Unlocking the Power of AI: Accessible Language Model Tuning for All

Cedric Clyburn & Legare Kerrison

Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.
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Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.

Jodie Burchell

Optimizing your AI/ML workloads for sustainability
46:31

Optimizing your AI/ML workloads for sustainability

Sohan Maheshwar

Hybrid AI: Next Generation Natural Language Processing
21:01

Hybrid AI: Next Generation Natural Language Processing

Jan Schweiger

Unboxing the DeepFace
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Unboxing the DeepFace

Sefik Serengil

What non-automotive Machine Learning projects can learn from automotive Machine Learning projects
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What non-automotive Machine Learning projects can learn from automotive Machine Learning projects

Jan Zawadzki

Mastering Image Classification: A Journey with Cakes
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Intelligent Automation using Machine Learning
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Boris Krumrey & Andreas Palfi & Radu Pruna

A beginner’s guide to modern natural language processing
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How Machine Learning is turning the Automotive Industry upside down
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Mastering Image Classification: A Journey with Cakes
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Scrape, Train, Predict: The Lifecycle of Data for AI Applications
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Vidas Bacevičius

The best of both worlds: Combining Python and Kotlin for Machine Learning
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Nils Kasseckert