Product details
Machine Learning & Deep Learning Workshop (9th & 10th Nov 2024)
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.
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.
