Rapid growth and remarkable success of machine learning can be witnessed by great advances in technology, contributing to the fields of healthcare, finance, agriculture, energy, education, transportation, and more. This course will emphasize on intuition and real- world applications (engineering, cybersecurity, healthcare) of Machine Learning (ML) rather than the statistics behind it. Key concepts of some popular ML techniques, including Naïve Bays, K-means clustering, nearest neighbor, feed-forward and recurrent networks, deep convolutional neural networks, Transformers, Generative models, etc., along with hands-on exercises will be provided to students. By the end of this course, students will be able to apply a variety of ML algorithms and utilize the associated Python libraries to practical problems, and build predictive models, evaluate and analyze results.
- Python for ML.
- Data preprocessing and Feature engineering
- Model Evaluation and optimization
- Text analysis with Naïve Bayes
- Data clustering using K-mean
- KNN
- Regression Analysis
- Simple to Deep Neural Networks
- Convolutional Neural Networks
- Transformers
- Generative Models
