A DataCamp course moving from scikit-learn into neural networks — regression, binary and multiclass classification, and a first look at deeper models — using Keras. Hands-on and project-driven, it is a practical next step for people who know some Python and machine learning and want to start building neural networks.
Good for: A hands-on step from classic ML into neural networks with Keras.
Less suitable if: You are new to Python/ML or want deep theory.
Skills you'll gain
Deep learningKerasNeural networksClassificationRegressionPython
Is this course right for you?
A good fit if you…
You know Python and some ML
You want hands-on neural networks
You like project-driven learning
Consider something else if you…
You are new to Python or ML
You want deep theory
You already build neural networks
Requirements: Python and basic ML (scikit-learn) familiarity.
Realistic time: Around 4 hours.
About this course
Introduction to Deep Learning with Keras moves from scikit-learn into neural networks: regression for predicting asteroid trajectories, binary classification for fake-bill detection, multiclass and multi-label problems, and a first look at autoencoders, CNNs, and LSTMs in the final chapter. It explicitly assumes you've completed Supervised Learning with scikit-learn first.
What you'll learn
Build regression, binary, multiclass, and multi-label neural networks
Use Keras callbacks for early stopping and training history
Interpret learning curves and detect overfitting
Apply batch normalization and tune hyperparameters
Build a basic autoencoder for image reconstruction
Get a first introduction to CNNs and LSTMs
This course includes
4h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription.
Comparison · LBS
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