Stanford University Machine Learning Specialization
This course provides a solid foundation in the concepts and techniques of machine learning,techniques and algorithms such as logistic regression, decision trees, random forests, neural networks, and deep learning.
What I have Learned:
During my completion of the Stanford University Machine Learning Specialization, I gained a strong understanding of the basics of supervised learning. This included linear and logistic regression, as well as neural networks. I also learned about different models for classification and regression tasks, such as decision trees and random forests. These concepts provided me with a solid foundation to build on as I progressed through the specialization.
In addition to supervised learning, I studied different techniques used in unsupervised learning. This included clustering, dimensionality reduction, and association rule learning. Algorithms such as k-means clustering, principal component analysis (PCA), and singular value decomposition (SVD) were also covered. Through this, I was able to explore and understand the complexities of unsupervised learning and how it can be applied to various scenarios.
My understanding of neural networks grew significantly during the course. I was able to dive deep into feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Furthermore, I gained insight into backpropagation, a key technique used to train neural networks. This allowed me to see the full potential of neural networks and how they can be used in various applications.
Finally, I was able to gain experience in applying machine learning techniques to real-world problems. Through various projects, I explored applications of machine learning such as natural language processing (NLP), computer vision, and recommender systems. I also learned about model selection and evaluation techniques, such as cross-validation and regularization, and studied methods for evaluating model performance, such as precision, recall, and F1 score. Overall, the Stanford University Machine Learning Specialization provided me with a comprehensive and rigorous introduction to the field of machine learning, enabling me to develop a strong foundation for continued learning and growth in this exciting field.