Machine Learning (Fundamentals)
This course establishes essential machine learning foundations for professionals entering the field. You'll gain hands-on experience with core algorithms, data preparation techniques, and model evaluation methods used in real-world applications.
### Course Content
* **Introduction to Machine Learning & Python for ML**: Master supervised and unsupervised learning fundamentals while building proficiency with Python libraries including NumPy, Pandas, Matplotlib, and Scikit-learn.
* **Data Preprocessing & Feature Engineering**: Learn to clean and transform real-world data, handle missing values and outliers, encode categorical variables, scale features, and engineer meaningful attributes that improve model performance.
* **Classical Machine Learning Algorithms**: Build and compare foundational models including linear/logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors, understanding when to apply each algorithm.
* **Model Evaluation & Optimization**: Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well.
### Audience
Analysts, software developers, and professionals with basic Python knowledge who want to transition into data science and machine learning roles.