📘 ML Fundamentals – Labs & Exercises
This section is based on mlcourse.ai, an open Machine Learning course by Yury Kashnitsky (yorko), created within the OpenDataScience (ods.ai) community.
Some datasets are sourced from Kaggle and other open resources.
👉 This is a self-study project, where I reproduced and experimented parts of the course to strengthen my understanding of the fast-moving field of machine learning.
📂 Topics Covered
- Pandas & Data Analysis → Titanic dataset exploration.
- Data Visualization → Matplotlib, Seaborn, Plotly for exploratory analysis.
- Decision Trees & k-NN → supervised learning basics.
- Linear Models → r Explored regression and classification with linear models: MSE and bias–variance tradeoff, logistic regression with likelihood learning, regularization techniques, and case studies such as analysis on IMDB movie reviews.
- Feature Engineering & Selection → Focused on preparing better data for ML models: creating useful features, transforming them for improved accuracy, and removing irrelevant ones.
- Ensembles (Random Forests, Bagging) → tree-based ensembles.
- Unsupervised Learning → PCA, clustering, k-means.
- Time Series → rolling window estimations, exponential smoothing, SARIMA.