Machine Learning Specialist

Machine learning includes many different fields that focus on different problems. Explore what machine learning is and the fundamentals of supervised learning.

Unsupervised learning can provide powerful insights on data without the need to annotate examples. Explore unsupervised learning, clustering, anomaly detection, and dimensional reduction.

The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Discover how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

Principal component analysis (PCA) is a must-know pre-processing technique for anyone working with machine learning (ML). Used to process data fed into ML models, PCA is useful in many scenarios, such as exploratory data analysis, dimensionality reduction, and latent feature extraction. Use this course to learn the basic intuition behind principal component analysis along with how to use PCA. Start by visualizing how principal components work. Then, examine how they can be computed mathematically using the eigenvectors and eigenvalues of the covariance matrix of the data. As you advance, manually compute principal components, view the re-oriented data, and compare this result with the principal components computed. Lastly, use PCA for dimensionality reduction to train a classification model. When you're done, you'll have the skills and knowledge to use PCA to build more robust machine learning models.

Users marvel at a system's ability to recommend items they're likely to appreciate. As someone working with machine learning, implementing these recommendation systems (also called recommender systems) can dramatically increase user engagement and goodwill towards your products or brand. Use this course to comprehend the math behind recommendation systems and how to apply latent factor analysis to make recommendations to users. Examine the intuition behind recommender systems before investigating two of the main techniques used to build them: content-based filtering and collaborative filtering. Moving on, explore latent factor analysis by decomposing a ratings matrix into its latent factors using the gradient descent algorithm and implementing this technique to decompose a ratings matrix using the Python programming language. By the end of this course, you'll be able to build a recommendation system model that best suits your products and users.