This Machine Learning Foundations training course focuses on the mathematics and algorithms used in Data Science. You will learn core skills and explore machine learning algorithms along with their practical application and limitations. With this knowledge, you will build the intuition necessary to solve complex machine learning problems.
By attending Machine Learning Foundations workshop, delegates will learn:
- Core machine learning mathematics and statistics
- Supervised Learning vs. Unsupervised Learning
- Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor
- Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, and k-Nearest Neighbors (KNN)
- Clustering Algorithms including k-Means, Fuzzy clustering, and Gaussian Mixture
- Neural Networks including Hidden Markov (HMM), Recurrent (RNN), and Long-Short Term Memory (LSTM)
- Dimensionality Reduction, Single Value Decomposition (SVD), and Principle Component Analysis (PCA)
- How to choose an algorithm for a given problem
- How to choose parameters and activation functions
- Ensemble methods
- Strong foundational mathematics skills in Linear Algebra and Probability
- Basic Python skills
- Basic Linux skills
- Familiarity with command line options such as ls, cd, cp, and su
The Machine Learning Foundations class is ideal for:
- Experienced Data Scientists, Data Analysts, Developers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.