Courses and Books
Welcome to the courses and books section of my portfolio. Here, I share the educational programs I have completed and the books I have studied to expand my professional skills. These courses and books complement my formal university education, reflecting my commitment to continuous learning beyond academia.
Courses
Machine Learning in Production
by DeepLearning.AI and Stanford Online, USA
February 2025
Skills: Model deployment, error analysis, data quality improvement, project scoping;
Overview
This course covered the four key stages of a machine learning project: Scoping, Data, Modeling, and Deployment, with a strong focus on practical guidelines for real-world applications.
- Scoping: Strategies for identifying impactful, addressable problems that drive business value.
- Data: Ensuring high-quality data through label consistency, data augmentation, feature engineering, and proper train/dev/test splits.
- Modeling: Establishing strong baselines, conducting error analysis, and auditing model performance.
- Deployment: Managing data and concept drift, implementing deployment patterns, and setting up effective monitoring systems.
Advanced Learning Algorithms
by DeepLearning.AI and Stanford Online, USA
February 2023
Skills: Neural networks, decision trees, Random Forest, XGBoost;
Overview
This course covered neural networks, decision trees, and tree-based ensemble
methods such as Random Forest and XGBoost, along with essential machine learning
strategies. I gained a deep understanding of how these algorithms work and built
them from scratch using Python. Additionally, the course explored key
hyperparameters and best practices for optimizing model performance.
Supervised Machine Learning: Regression and Classification
by DeepLearning.AI and Stanford Online, USA
February 2023
Skills: Linear and logistic regression, machine learning basics;
Overview
This course began with linear regression as a fundamental example of regression,
followed by logistic regression as an introduction to classification. Along the
way, key machine learning concepts were covered, including bias-variance
tradeoff, dataset splitting (train/dev/test sets), and other foundational
principles essential for building and evaluating models.
Books
Introduction to Statistics
by David Lane, Rice University, USA
Skills: distributions, probability, estimation, hypothesis testing, power, regression, transformations, chi square, effect size, research design, etc.;
Overview
I spent many hours deeply engaging with this book, which covers all fundamental
topics in statistics, from graphing and summarizing distributions to
probability, hypothesis testing, regression, and ANOVA. Working through each
chapter strengthened my intuition for statistical concepts, giving me a solid
foundation for data analysis, experimental design, and statistical inference.
Learn Data Mining Through Excel
by Hong Zhou, University of Saint Joseph, USA
Skills: machine learning algorithms;
Overview
This ebook provided a hands-on approach to data mining by implementing
algorithms
manually in Excel, forcing me to break down each step and truly understand their
mechanics. Unlike automated tools, Excel makes every calculation visible,
reinforcing intuition for methods like linear and logistic regression, decision
trees, neural networks, k-NN, Naïve Bayes, sentence sentiment analysis, and
more.