Machine Learning (Unsupervised Learning) Part 3 via MATLAB

Unsupervised learning model is used where we only have input/features/attributes and no info regarding output. In such cases, Unsupervised model makes cluster/group of observation based upon their similarity of features. The implementation is shown above in MATLAB (Kmeans Cluster).

Objective: The main objectives of the program is to:
1. Develop the conceptual and fundamental concept of Data Analytics elements.
2. Develop a basic understanding of all key component of MATLAB for Data Analytics and AI development.
3. Develop understanding of Data Science and various feature extraction techniques.
4. Understand all different types of Machine learning-based algorithm.
5. Understand the performance and limitation of AI and ML algorithm.
6. Develop ML/ AI algorithm for Smart Application development.

Outcome: On completion of the course students will be expected to:
1. Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
2. Have an understanding of the steps used in Data Extraction, Cleaning and Pre-processing etc.
3. Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning.
4. Be able to design and implement various machine learning algorithms in a range of real-world applications.

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