Applied Machine Learning Training Course
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.
Course Outline
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
Requirements
- Basic understanding of statistics and data analysis
- Programming experience in R, Python, or other relevant programming languages
Audience
- Data scientists
- Statisticians
Open Training Courses require 5+ participants.
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Testimonials (5)
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
Many examples and exercises related to the topic of the training.
Tomasz - Ministerstwo Zdrowia
Course - Advanced R Programming
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
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