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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
14 Hours
Testimonials (5)
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
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 variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
A lot of knowledge - theoretical and practical.
Anna Alechno
Course - Forecasting with R
I genuinely enjoyed working 1:1 with Gunner.
