Machine Learning Specialist

The course takes you through the full machine learning workflow. You start with Python and data analysis, build the mathematical understanding behind the algorithms, then move through supervised learning, feature engineering and unsupervised learning. The final section covers advanced model improvement techniques including ensemble methods and recommender systems.

• Clean and analyze data with Python, pandas and seaborn
• Build and evaluate Linear and Logistic Regression, SVM and Decision Tree models
• Apply feature selection, regularization and transformation techniques
• Cluster data with K-Means, hierarchical and DBSCAN algorithms
• Tune models with grid search, Bayesian optimization and boosting methods

Throughout the course you work on real datasets: predicting credit card fraud, classifying medical data, clustering handwritten digits and building recommender systems. Each project is something you can add to your portfolio and walk through in a job interview.