Applied Machine Learning

• Basic understanding of statistics and data analysis
• Programming experience in R, Python, or other relevant programming languages

Audience

• Data scientists
• Statisticians

The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Foundations of Machine Learning

• Introduction to Machine Learning concepts and workflows
• Supervised vs. unsupervised learning
• Evaluating machine learning models: metrics and techniques

Bayesian Methods

• Naive Bayes and multinomial models
• Bayesian categorical data analysis
• Bayesian graphical models

Regression Techniques

• Linear regression
• Logistic regression
• Generalized Linear Models (GLM)
• Mixed models and additive models

Dimensionality Reduction

• Principal Component Analysis (PCA)
• Factor Analysis (FA)
• Independent Component Analysis (ICA)

Classification Methods

• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM) for regression and classification
• Boosting and ensemble models

Neural Networks

• Introduction to neural networks
• Applications of deep learning in classification and regression
• Training and tuning neural networks

Advanced Algorithms and Models

• Hidden Markov Models (HMM)
• State Space Models
• EM Algorithm

Clustering Techniques

• Introduction to clustering and unsupervised learning
• Popular clustering algorithms: K-Means, Hierarchical Clustering
• Use cases and practical applications of clustering

Summary and Next Steps