Machine Learning
218 learners
Modeling the Iris Dataset with TensorFlow
Explore the famous Iris dataset in our advanced TensorFlow course. Learn to preprocess data, build, and train a multi-class classifier. Evaluate performance with metrics and visualizations. Conclude with model optimization techniques to boost efficiency and accuracy, and cover saving/loading for deployment.
Python
Scikit-learn
TensorFlow
5 lessons
25 practices
3 hours
Deep Learning and Neural Networks
Lessons and practices
Exploring and Preprocessing the Iris Dataset
Changing Train-Test Split Ratio
Fix the Data Preprocessing Bugs
Hands-on Data Preprocessing
End-to-end Preprocessing the Iris Dataset
Multi-Class Model Training Basics
Changing Training Parameters in TensorFlow
Fixing TensorFlow Model Training
Building a TensorFlow Model
Implementation of a TensorFlow Model
Understanding Model Performance Evaluation
Visualizing Accuracy for Model Evaluation
Fixing Bugs in TensorFlow Evaluation
Evaluate Model Accuracy and Loss
Visualizing Model Performance and Evaluation
Early Stop on Training with TensorFlow
Modify Early Stopping Parameters
Fix TensorFlow Early Stopping Code
Initialize Early Stopping Callback
Implement Early Stopping in TensorFlow
Model Saving and Loading Basics with TensorFlow
Changing Saved Model's Name
Fix Model Saving and Loading
Implementing Save and Load in TensorFlow
Save, Load, and Verify Models
Meet Cosmo:
The smartest AI guide in the universe
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal
