Machine Learning Foundation (Math Emphasis) | Exploring Statistics, Algorithms and Neural Networks (TTML5504)

Machine Learning Foundation is a hands-on introduction to the mathematics and algorithms used in Data Science, as well as creating the foundation and building the intuition necessary for solving complex machine learning problems. The course provides a good kick start in several core areas with the intent on continued, deeper learning as a follow on. This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations and practical application of these methods in a machine learning environment.

Retail Price: $2,195.00

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Course Days: 3


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Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations and practical application of these methods in a machine learning environment. This course reviews key foundational mathematics and introduces students to the algorithms of Data Science. 

Working in a hands-on learning environment, students will explore:

  • Popular machine learning algorithms, their applicability and limitations
  • Practical application of these methods in a machine learning environment
  • Practical use cases and limitations of algorithms
  • 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, k-Nearest Neighbors (KNN)
  • Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture
  • Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM)
  • Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA)
  • How to choose an algorithm for a given problem
  • How to choose parameters and activation functions
  • Ensemble methods

 

Course Prerequisites

Although this course is highly technical in nature, it is a foundation-level machine learning class for Intermediate skilled team members who are relatively new to AI and machine learning. This course as-is is not for advanced participants.

This course is geared for Data Analysts, Programmers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.  Attending students should have

  • Strong foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
  • Basic Python Skills.  Attendees without Python background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in R or Scala – please inquire for details)
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

 


Course Agenda

 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

  1. Core Machine Learning Mathematics Review
  • Statistics Overview and Review
  • Mean, Median, Variance, and deviation
  • Normal / Gaussian Distribution
  1. Probability Review
  • Probability Theory
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Measure-Theoretic Probability Theory
  • Central Limit and Normal Distribution
  • Probability Density Function
  • Probability in Machine Learning
  1. Supervised Learning
  • Supervised Learning Explained
  • Classification vs. Regression
  • Examples of Supervised Learning
  • Key supervised algorithms
  1. Unsupervised Learning
  • Unsupervised Learning
  • Clustering
  • Examples of Unsupervised Learning
  • Key unsupervised algorithms (overview)
  1. Regression Algorithms
  • Linear Regression
  • Logistic Regression
  • Support Vector Regression
  • Decision Trees
  • Random Forests
  1. Classification Algorithms
  • Bayes Theorem and the Naïve Bayes classifier
  • Support Vector Machines
  • Discriminant Analysis
  • k-Nearest Neighbor (KNN)
  1. Clustering Algorithms
  • k-Means Clustering
  • Fuzzy Clustering
  • Gaussian Mixture Models
  1. Neural Networks
  • Neural Network Basics
  • Hidden Markov Models (HMM)
  • Recurrent Neural Networks (RNN)
  • Long-Short Term Memory Networks (LSTM)
  1. Choosing Algorithms
  • Choosing between Supervised and Unsupervised algorithms
  • Choosing between Classification Algorithms
  • Choosing between Regressions
  • Choosing Neural Networks
  • Choosing Activation Functions
  1. Ensemble Methods
  • Ensemble Theory and Methods
  • Ensemble Classifiers
  • Bucket of Models
  • Boosting
  • Stacking
  1. Optional: Topics Survey
  • Machine Learning in Python: NumPy, Pandas, SciKit-ML, and MatPlotLIb; NLTK, Keras
  • Machine Learning in R
  • Machine Learning in Java
  • Machine Learning with Apache Madlib
  • Hadoop, MapReduce, and Mahout
  • Spark and MLLib
  • TensorFlow


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