Machine Learning Foundations | Working with Statistics, Algorithms, and Neural Networks | Math Emphasis
Machine Learning Foundations | Working with Statistics, Algorithms, and Neural Networks | Math Emphasis Course Details:
This foundation-level hands-on course focuses on the mathematics and algorithms used in Data Science. You’ll learn core skills and explore machine learning algorithms along with their practical application and limitations. With this knowledge, you’ll build the intuition necessary to solve complex machine learning problems.
*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
Join an engaging hands-on learning environment, where you’ll 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
This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.
Before attending this course, you should have:
- 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
This course is for intermediate skilled professional. This is not a basic class.
Experienced Data Scientists, Data Analysts, Developers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.
This course focuses on the mathematics aspect of machine learning as opposed to more general skills and concepts. It is also offered using R or Scala – please inquire for details.