I learned how to build a customized chatbot using OpenAI models like GPT-3.5 and GPT-4. I developed skills in API integration, fine-tuning models, and optimizing chatbot performance. Additionally, I practiced integrating the chatbot into platforms like Telegram and gained insights into adjusting parameters for more natural conversational experiences.

I learned how to craft precise prompts to get better responses from AI models. The course taught me techniques like zero-shot and chain-of-thought prompting, as well as how to optimize prompts for different tasks. I also practiced using ChatGPT to automate workflows and create custom solutions, improving my efficiency in everyday tasks and professional projects.

I reinforced my skills in understanding and exploring datasets through visualizations and statistical methods. I revisited key concepts like univariate, bivariate, and multivariate analysis, using Python libraries like Pandas, Matplotlib, and Seaborn. I also practiced identifying patterns, cleaning data, and deriving insights to prepare for modeling, reinforcing the importance of EDA in setting up successful machine learning workflows​.

I refreshed my understanding of key programming concepts using one of the most popular languages for web and game development. The course covered the basics of C# syntax, data types, variables, and operators. I also practiced working with control structures such as conditionals and loops, and got hands-on experience with Visual Studio, which I used to build my first console and graphical applications. Additionally, I reinforced my understanding of methods, arrays, and lists.

I gained a deeper understanding of setting up an efficient workspace for data science projects. I learned how to install and configure Jupyter Notebooks, manage virtual environments with Anaconda, and use key tools like VSCode for better productivity. Additionally, I reinforced my knowledge of creating and managing virtual environments to isolate projects and streamline dependencies, ensuring my projects are reproducible and scalable for future work.

I strengthened my understanding of how to make predictions and inferences from sample data. The course covered key topics like hypothesis testing, confidence intervals, and sampling techniques, all applied using Python libraries like Pandas and Scipy. I also revisited concepts like the Central Limit Theorem, t-tests, ANOVA, and bootstrapping to validate data assumptions and build more reliable machine learning models.

I reinforced my skills in navigating and managing a UNIX-based system through the terminal. I revisited essential commands like ls, cd, pwd, and learned more advanced techniques such as managing permissions, piping outputs, and handling background processes. The course also covered file compression, search tools like grep, and personalizing the terminal for a more efficient workflow, boosting my productivity as a developer.

I learned to apply the five key SOLID principles to improve code structure. These principles ensure my code is cleaner, more maintainable, and scalable by focusing on responsibilities, extensibility, and decoupling through abstraction.

I deepened my understanding of how to implement the core pillars of object-oriented programming (OOP) in TypeScript. I practiced using key concepts such as inheritance, encapsulation, and polymorphism, and applied design patterns like the Singleton. Additionally, I worked with asynchronous code, using promises to consume REST APIs, and explored generics for more flexible and reusable code. This course enhanced my ability to write cleaner, more maintainable TypeScript code.

I solidified my understanding of Python functions, including lambda functions, higher-order functions, and handling multiple returns. I revisited comprehensions to simplify my code, practiced refactoring using functional approaches like map, filter, and reduce, and worked on better error handling. This course sharpened my ability to write cleaner, more efficient Python code while improving error management in my projects.

I reinforced my knowledge of how to leverage data to drive business decisions. I learned to efficiently structure business analyses using storytelling techniques and explored how to integrate business intelligence, machine learning, and big data to create strategies that enhance company performance. The course emphasized transforming data into actionable insights, helping me build a strong foundation for data-driven decision-making.

I reinforced my understanding of essential math concepts such as basic arithmetic operations, exponentiation, and algebraic principles. I also revisited topics like functions, polynomials, and graphing, which form the foundation for more advanced subjects. The course improved my confidence in applying these concepts to real-world problems, and the step-by-step explanations made it easy to follow along and practice effectively​.

I reinforced my knowledge of the essential concepts of artificial intelligence (AI) and machine learning (ML). The course covered how AI tools like ChatGPT and DALL-E function, as well as their real-world applications. I also explored key topics like data science workflows, subfields of AI, and the role of deep learning. This course helped me better understand the foundational elements of AI and its practical uses in various industries​.

I reinforced key principles in computing, including how processors, memory, and systems like CPUs, RAM, and storage devices function. I explored how the internet operates, understanding protocols, IP addresses, and DNS. The course also covered essential topics in operating systems, from the differences between Windows, Linux, and macOS to embedded systems like IoT. Additionally, I revisited basic concepts of data structures and file formats, such as how JPG and ZIP work, enhancing my knowledge of core software engineering foundations.

I strengthened my ability to build predictive models using this powerful Python library. I learned to train models with labeled data and make predictions on real-world tasks like customer churn, diabetes detection, and song genre classification. The course also covered how to tune model parameters and evaluate performance on unseen data, giving me practical experience with real datasets and improving my machine learning workflow.

I reinforced the core concepts of programming using Python. The course covered essential topics such as variables, data types (like strings, numbers, booleans), conditionals, loops, and lists.

I solidified my understanding of Python’s foundational concepts, including variables, data types, conditionals, loops, and basic functions. I also practiced using lists and dictionaries for data organization and wrote small programs to apply control flow structures.

I strengthened my understanding of Git as a version control system and learned how to manage projects more efficiently using GitHub. The course covered key concepts like creating repositories, making commits, managing branches, and handling merges. Additionally, I practiced resolving conflicts, using pull requests, and collaborating with other developers on GitHub. This course equipped me with the skills to confidently manage code versioning in both local and remote repositories.

I deepened my understanding of the core concepts of machine learning, including supervised and unsupervised learning, data preprocessing, and feature engineering. I also learned how to utilize AWS tools like SageMaker to build, train, and deploy machine learning models at scale. The course provided hands-on experience in model evaluation, tuning, and deployment, helping me gain practical skills in using AWS for real-world machine learning applications.

I reinforced my skills in creating insightful data visualizations. I learned how to use Matplotlib to generate various types of plots, such as line graphs, bar charts, and histograms, and customize them to represent different datasets. The course also covered how to automate and share visualizations, making the process efficient for communicating data patterns and insights to stakeholders, enhancing the overall analysis process.

I learned the fundamentals of R, including how to work with vectors, factors, lists, and data frames. The course helped me develop essential coding skills to handle data analysis tasks and laid the foundation for more advanced analyses, like machine learning and financial modeling, using this versatile language. This introduction provided a solid base for applying R in real-world data science projects.