This course provided a comprehensive introduction to both classical machine learning and deep learning, covering theoretical foundations and practical applications. I learned to understand and apply fundamental concepts in machine learning and deep learning, and gained the ability to evaluate and compare different classification and regression models.
Part I: Machine Learning Fundamentals
I explored various classical machine learning models including linear models, decision theory, kernel methods, and sparse kernel machines. The course covered Bayesian models, Support Vector Machines (SVM), decision trees, random forests, K-Nearest Neighbors (KNN), Naive Bayes, and ensemble methods including bagging, boosting, and stacking. I also worked extensively with linear and logistic regression, understanding their applications and limitations.
Part II: Deep Learning Techniques
The deep learning portion covered core concepts including machine learning basics, feedforward networks, regularization, optimization, convolutional networks, and sequence modeling. I learned about training methodologies and how to apply these techniques to real-world applications.
Throughout the course, I implemented and optimized machine learning algorithms using Python and relevant libraries, developing practical skills in model development and fine-tuning. I learned to analyze the strengths and weaknesses of different machine learning techniques and apply them to real-world datasets.
By the end of the course, I was able to develop and fine-tune deep learning models for various applications, gaining hands-on experience with modern ML frameworks. This course has provided me with a solid foundation in both classical and modern machine learning approaches, preparing me for advanced research and practical applications in AI.