Product details
Machine Learning & Deep Learning Workshop (9th & 10th Nov 2024)
Workshop Details:
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Date: 9th & 10th Nov 2024
•
Time: 2 pm - 6 pm
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Duration: 8hrs
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Age group: 18+
What we will cover:
Day 1 (9th Nov 2024)
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Scikit-learn Basics: Introduction to essential functions for datasets, preprocessing, model building, and evaluation.
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Regression Models: Build and evaluate regression models using metrics like MSE, MAE, and R-squared.
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Classification Models: Implement classification algorithms and assess performance with accuracy, precision, recall, and F1 score.
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Clustering Models: Explore unsupervised learning with K-means, evaluating clusters using inertia, silhouette score, and Davies-Bouldin index.
Day 2 (10th Nov 2024)
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Prerequisites: Python and basic machine learning knowledge.
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TensorFlow Basics: Introduction to TensorFlow concepts and essential functions like tf.data, tf.Variable, tf.Tensor, and tf.keras.
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Regression Model: Build and evaluate a regression model using TensorFlow with metrics such as MSE, MAE, and R-squared.
•
Classification Model: Implement a binary or multiclass classification model with TensorFlow, assessing performance using accuracy, precision, recall, and F1 score.
To Learn More click here!
Note: This is a hands-on workshop. We will share the list of required software once you register. No kits are required.
Workshop Details:
•
Date: 9th & 10th Nov 2024
•
Time: 2 pm - 6 pm
•
Duration: 8hrs
•
Age group: 18+
What we will cover:
Day 1 (9th Nov 2024)
•
Scikit-learn Basics: Introduction to essential functions for datasets, preprocessing, model building, and evaluation.
•
Regression Models: Build and evaluate regression models using metrics like MSE, MAE, and R-squared.
•
Classification Models: Implement classification algorithms and assess performance with accuracy, precision, recall, and F1 score.
•
Clustering Models: Explore unsupervised learning with K-means, evaluating clusters using inertia, silhouette score, and Davies-Bouldin index.
Day 2 (10th Nov 2024)
•
Prerequisites: Python and basic machine learning knowledge.
•
TensorFlow Basics: Introduction to TensorFlow concepts and essential functions like tf.data, tf.Variable, tf.Tensor, and tf.keras.
•
Regression Model: Build and evaluate a regression model using TensorFlow with metrics such as MSE, MAE, and R-squared.
•
Classification Model: Implement a binary or multiclass classification model with TensorFlow, assessing performance using accuracy, precision, recall, and F1 score.
To Learn More click here!
Note: This is a hands-on workshop. We will share the list of required software once you register. No kits are required.
