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.

want to work with data and machine learning and are looking for a structured path from the foundations all the way to advanced model building, with real projects along the way. have some experience with coding or data and want to develop practical machine learning skills that are directly applicable across tech, analytics, and AI-driven roles.

Data Science Foundations

• Clean, analyze, and visualize data with Python
• Work with pandas and seaborn for real-world datasets
• Build your first data projects for your portfolio

Supervised learning

• Build and evaluate Linear and Logistic Regression models
• Work with Naive Bayes, SVM, KNN, and Decision Trees
• Predict outcomes on real data like fraud and census records

Unsupervised learning

• Cluster data with K-Means, DBSCAN, and more
• Reduce dimensionality with PCA
• Recognize handwritten digits and classify telescope data

Math for Machine learning

• Understand probability, statistics, and linear algebra
• Apply calculus concepts to real-world data analysis
• Build the mathematical intuition behind ML algorithms

Feature Engineering

• Select features with filter and wrapper methods
• Transform numerical data to improve model performance
• Measure feature importance with regularization and trees

Advanced Communicating with Data

• Tune models with grid search and Bayesian optimization
• Build stronger models with Random Forests and boosting
• Build and deploy recommender systems with Python

Partner:

Nova Learning skaber adgang til de bedste ressourcer inden for læring: e-learning, mentorordning, eksamenssimulering og virtuelle labs. Alle e-learning kurser er tilpasset og målrettet mod certificeringer og/eller rettet mod jobs inden for de nyeste teknologier. Herunder Artificial Intelligence, Machine Learning & Data Science.