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World Congress 2024 • Aug 20, 2024 • Session details

Machine learning 101: Where to begin?

Lutske van der Meer

Are you using machine learning just for the hype? Learn the foundational Python workflows, preprocessing techniques, and essential algorithms needed to confidently build your first model.

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#1 about 3 min

Introduction to machine learning with a practical cat problem

Over-engineering a pet food dispenser illustrates the basic motivation for exploring machine learning.

#2 about 2 min

Distinguishing between AI, machine learning, and deep learning

Machine learning teaches computers without explicit programming while deep learning relies on neural networks to process specific tasks.

#3 about 3 min

Understanding supervised, unsupervised, and reinforcement learning models

Different learning types vary from labeling known data to categorizing raw data and rewarding actions in dynamic environments.

#4 about 3 min

Evaluating whether machine learning is necessary for business goals

Relying on existing business rules often proves more cost-effective than investing extensive time and domain expertise into AI hype.

#5 about 3 min

Setting up Python libraries and sourcing initial datasets

Essential data science libraries like scikit-learn and pandas enable processing available datasets from platforms like Kaggle.

#6 about 2 min

Downloading and inspecting data frames via the Kaggle API

Authenticating with Kaggle to download a raw target dataset reveals common formatting inconsistencies and missing values requiring cleanup.

#7 about 3 min

Cleaning missing values and expanding datasets with data augmentation

Applying label encoders and generating synthetic images prevents inadequate training sizes while requiring care to avoid over-fitting.

#8 about 2 min

Splitting data for training, validation, and testing stages

Dividing datasets securely allows iterative learning and final independent accuracy validations before real-world deployment.

#9 about 3 min

Exploring common regression and classification machine learning algorithms

Foundational techniques span from fitting simple linear regression lines to using random forest classifiers for majority-ruled category predictions.

#10 about 5 min

Evaluating model accuracy with confusion matrices and mean squared error

Visualizing over-fitting limits and calculating statistical errors helps ground raw prediction values into tangible business impacts.

#11 about 2 min

Refining models by managing outliers and sourcing more data

Strategies like trimming extreme outliers via winsorizing actively enhance accuracy when preliminary outputs miss intended targets.

#12 about 2 min

Real life recommendation systems and final project conclusions

Examining proven recommendation setups anchors core lessons around thoughtful data collection and strict model verification.

Matching moments

4:44 min

Introduction to prototyping and building practical AI applications

Krzysztof Cieślak Krzysztof Cieślak · WWC 2024

1:07 min

Navigating the complexities of machine learning model lifecycles

Iryna Kondrashchenko Iryna Kondrashchenko +1 · WWC 2025

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

4:33 min

Core stages of training and managing machine learning models

Boris Krumrey +2 · LIVE

3:45 min

Familiarizing with machine learning and neural network basics

Tillman Radmer +2 · WWC 2021

2:26 min

Understanding the machine learning building workflow

Marco Zamana · LIVE

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