Applications of Machine Learning in Healthcare, Finance, Industry and E-commerce Sectors
This course provides an insight to applications of Machine Learning in real world. Designed for students with a keen interest in technology and problem-solving, the course will cover concepts such including supervised and unsupervised learning, data preprocessing, model training, and evaluation. The students will explore the practical applications of machine learning in fields such as healthcare, finance, and e-commerce
Learning Outcomes
On completion, students will be able to:
- Understand Machine Learning Concepts
- Apply various data preprocessing techniques for analysing the data
- Apply various Machine learning models in real world scenarios
Course Contents (Academic schedule)
Introduction to Machine Learning, Exploratory Data Analysis (EDA), Supervised Learning Techniques: Baseline and Ensemble models, Decision Trees and Random Forests, Evaluation Metrics, Unsupervised Learning: Clustering Techniques: K-Means and DBSCAN, Dimensionality Reduction using PCA, Evaluating Clustering Results, Case Studies in Healthcare, Finance, and E-Commerce
- Essential linear algebra for deep learning
- Fundamentals of linear classification: weights, bias, scores, and loss functions
- Calculus for the gradient descent algorithm
- Forward and backward propagation with regularization
- Batch processing for large datasets
- Linear to nonlinear classification via activation functions
- Computational setup of a shallow neural network
- Tuning neural network performance
- Pre-processing data and batch normalization
- Cross-validation for validating model performance
- Extending the computational setup from a shallow to a deep neural network
- Introduction to the TensorFlow library
- Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.
Pre-requisites
- Python Programming
Name of the Coordinator
Program Highlights
- Core Machine Learning Concepts
- Practical Skills in Data Analysis
- Real-World Applications of ML Across Healthcare, Finance, Industry, and E-Commerce