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.