Throughout the course, I learned,

• The fundamentals of machine learning, including what it is, the different types of machine learning algorithms, and their applications
• How to implement and apply linear regression, which is a basic machine learning algorithm used to predict continuous outcomes
• How to use logistic regression, which is another type of supervised learning algorithm that can be used for classification problems
• How to build artificial neural networks, which is a type of machine learning model that is inspired by the structure of the human brain and is capable of performing complex tasks
• How to perform model selection and regularization to avoid overfitting and improve model performance
• How to use support vector machines (SVMs) to classify data by finding the best hyperplane that separates different classes of data
• How to use k-means clustering to segment data into groups or clusters based on similarities in their features
• How to use principal component analysis (PCA) to reduce the dimensionality of data by identifying the most important features and discarding those that are less important
• How to use gradient descent and backpropagation to train neural networks and optimize their performance
• How to apply machine learning algorithms to real-world problems
• How to use convolutional neural networks (CNNs) to analyze images and perform tasks
• The importance of ethical considerations in machine learning, including issues related to bias, fairness, privacy, and transparency

In this course, I learned,

• Understanding what siamese networks are and how they work
• Implementing a siamese network from scratch using PyTorch
• Understanding the concept of similarity and distance metrics, and how to use them in a siamese network
• Learning about transfer learning and how to use pre-trained models in a siamese network
• Learning about advanced techniques like triplet loss, and how to use them in a siamese network
• Understanding how to evaluate the performance of a siamese network
• Gaining practical experience by working on a real-world project using siamese networks and PyTorch

Throughout the course, I learned,

• The principles of object-oriented design, including encapsulation, inheritance, and polymorphism
• The importance of designing classes that are cohesive, with a single, well-defined responsibility
• The use of design patterns to solve common problems in object-oriented programming
• Techniques for designing flexible and extensible software, including using interfaces and abstract classes
• The benefits of modular design and how to create software modules that can be easily reused and tested
• The role of design in creating high-quality software that is maintainable and easy to change over time

In this course, I learned,

• Basics of relational databases and SQL
• How to design and create a relational database schema
• How to write SQL queries to retrieve data from a database
• How to perform complex queries using joins, subqueries, and aggregations
• How to use SQL to modify and manipulate data in a database
• How to use views, indexes, and transactions to optimize database performance and ensure data integrity

In this course, I learned,

• The basics of Git version control system
• How to create and manage Git repositories
• How to use Git for distributed development
• How to collaborate with other developers using Git
• How to resolve conflicts that may arise during collaboration
• Best practices for using Git in real-world projects
• How to use different Git workflows, such as feature branching and pull requests

Throughout the course, I learned,

• How to work with different Linux tools, including the GNU Compiler Collection (GCC), the GNU Debugger (GDB), and the make build automation tool
• How to use tools like strace, ltrace, and valgrind to debug and profile programs
• How to work with Linux system calls and libraries, including dynamic linking and the ELF file format
• How to use the Git version control system to manage your code and collaborate with others
• How to work with Bash scripts, including writing scripts to automate tasks and parse text data

In this course, I learned,

• The basics of Linux operating system and how to use command-line interface to navigate through the file system, create and manipulate files and directories, and manage user accounts and permissions
• How to use common Linux development tools such as Git, Make, GCC, and GDB
• How to write shell scripts to automate tasks and use regular expressions to manipulate text
• How to use Linux system calls to write simple C programs and create processes, manage memory, and communicate between processes using pipes and signals
• How to set up a development environment for Linux applications using virtual machines and Docker containers
• How to write and run simple web applications using Apache and Nginx web servers, PHP, and Python
• How to secure Linux systems using firewalls, SSH, and HTTPS protocols

Throughout the course, I learned,

• The importance and benefits of open-source software development
• How to contribute to open-source projects using Git and GitHub
• Techniques for writing effective documentation
• Strategies for collaborating with other developers and contributing to a project’s codebase
• The different open-source licenses and their implications for software distribution and modification
• Best practices for building a strong and inclusive open-source community
• The role of open-source in various industries and its impact on innovation and technology

Throughout the course, I learned,

• HTML and CSS for building web pages with responsive designs
• JavaScript for dynamic and interactive web development
• React library for building single-page applications
• Firebase for authentication, real-time database, and storage
• Netlify for hosting and deploying web applications
• Bootstrap for creating responsive and mobile-first designs
• jQuery for manipulating the HTML document and handling events
• RESTful API and AJAX for client-server communication
• Node.js and Express for building backend APIs
• MongoDB for NoSQL database management
• Several projects to apply the learned concepts and skills