Lutske De Leeuw
Machine learning 101: Where to begin?
#1about 4 minutes
Understanding core machine learning concepts and types
Distinguish between AI, machine learning, and deep learning, and explore the four main approaches: supervised, unsupervised, semi-supervised, and reinforcement learning.
#2about 2 minutes
Why you must define the problem first
Before coding, it is crucial to define the problem you are solving and determine if machine learning is the right solution over simpler business rules.
#3about 4 minutes
Collecting and exploring your initial dataset
Discover where to find public datasets like Kaggle and use Python to perform an initial exploration of your data to identify issues like missing values.
#4about 3 minutes
Preparing and augmenting data for training
Learn to clean, transform, and expand your dataset using techniques like feature encoding and data augmentation while avoiding the common pitfall of overfitting.
#5about 4 minutes
Splitting data and selecting a model algorithm
Properly divide your data into training and testing sets, then get an overview of common algorithms like regression, decision trees, and random forests.
#6about 6 minutes
Evaluating and improving your model's performance
Use tools like the confusion matrix and metrics like mean squared error to assess your model's accuracy and apply techniques for improvement, such as handling outliers.
#7about 2 minutes
Real-world applications and key takeaways
See how companies like Netflix use machine learning and review the key steps for starting your own ML project, from data collection to model verification.
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Matching moments
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Establishing a single source of truth for all data
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Why corporate AI adoption lags behind the hype
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Moving beyond headcount to solve business problems
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The future of recruiting beyond talent acquisition
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Incentivizing automation with a 'keep what you kill' policy
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