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. 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.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning 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

1

  • CORE MACHINE LEARNING MATHEMATICS REVIEW

  • Statistics Overview and Review

    Mean, Median, Variance, and deviation

    Normal / Gaussian Distribution

2

  • 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

3

  • SUPERVISED LEARNING

  • Supervised Learning Explained

    Classification vs. Regression

    Examples of Supervised Learning

    Key supervised algorithms

4

  • UNSUPERVISED LEARNING

  • Unsupervised Learning

    Clustering

    Examples of Unsupervised Learning

    Key unsupervised algorithms (overview)

5

  • REGRESSION ALGORITHMS

  • Linear Regression

    Logistic Regression

    Support Vector Regression

    Decision Trees

    Random Forests

6

  • CLASSIFICATION ALGORITHMS

  • Bayes Theorem and the Naïve

    Bayes classifier

    Support Vector Machines

    Discriminant Analysis

    k-Nearest Neighbor (KNN)

7

  • CLUSTERING ALGORITHMS

  • k-Means Clustering

    Fuzzy Clustering

    Gaussian Mixture Models

8

  • NEURAL NETWORKS

  • Neural Network Basics

    Hidden Markov Models (HMM)

    Recurrent Neural Networks (RNN)

    Long-Short Term Memory

    Networks (LSTM)

9

  • CHOOSING ALGORITHMS

  • Choosing between Supervised and

    Unsupervised algorithms

    Choosing between Classification

    Algorithms

    Choosing between Regressions

    Choosing Neural Networks

    Choosing Activation Functions

10

  • ENSEMBLE METHODS

  • Ensemble Theory and Methods

    Ensemble Classifiers

    Bucket of Models

    Boosting

    Stacking

11

  • 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

Audience

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

Language

English

Prerequisites

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 Students should have attended or have incoming skills equivalent to those in this course: Strong basic Python Skills. Attendees without Python background may view labs as follow along exercises or team with others to complete them. Good foundational mathematics in Linear Algebra and Probability Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

$2,395

Length: 3.0 days (24 hours)

Level:

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