Merge pull request #5 from sanggusti/feat/ilt4

Feat/ilt4

ILT4-unsupersived-learning-ann/README.md

@@ -0,0 +1,26 @@
+# ILT 4 - Unsupervised Learning and ANN
+
+This module covers the concepts and applications of unsupervised learning and artificial neural networks (ANN). It includes hands-on exercises with clustering algorithms and building ANN models using Python and popular machine learning libraries.
+
+## Notebooks
+
+- **Clustering_Lab_ILT_4.ipynb**: This notebook demonstrates various clustering algorithms, including K-Means, Agglomerative Clustering, DBSCAN, and more. It covers data preprocessing, model training, and evaluation.
+- **ann_sample.ipynb**: This notebook focuses on building a simple artificial neural network (ANN) using TensorFlow and Keras. It includes data preprocessing, model building, training, and evaluation.
+- **Recommendation_System.ipynb**: This notebook covers the basics of building a recommendation system using collaborative filtering and content-based filtering techniques. It includes data preprocessing, model building, and evaluation.
+- **System_Recommendation_Hands_On.ipynb**: This notebook provides a hands-on exercise for building a recommendation system. It guides you through the process of data preprocessing, model training, and evaluation using real-world datasets.
+
+## Key Concepts
+
+- **Unsupervised Learning**: A type of machine learning where the model is trained on unlabeled data.
+- **Clustering**: Grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
+- **Artificial Neural Networks (ANN)**: Computing systems inspired by the biological neural networks that constitute animal brains.
+- **Recommendation Systems**: Systems designed to recommend items to users based on various algorithms and data analysis techniques.
+- **Collaborative Filtering**: A method of making automatic predictions about the interests of a user by collecting preferences from many users.
+- **Content-Based Filtering**: A method of recommending items based on the features of the items and a profile of the user's preferences.
+
+## Libraries Used
+
+- `scikit-learn`: A machine learning library for Python that provides simple and efficient tools for data mining and data analysis.
+- `pandas`: A data manipulation and analysis library for Python.
+- `numpy`: A library for numerical computations in Python.
+- `tensorflow` and `keras`: Libraries for building and training neural networks.