Analytics and AI: Machine Learning

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Analytics and AI: Machine Learning

Course Description

Machine learning (ML) is comprised of a set of AI tools that give computers the ability to learn without being explicitly programmed. This Machine Learning training course provides learners with a thorough grounding in the methods, theory, mathematics, and algorithms required to apply ML in practice, customize ML approaches based on the requirements of a situation, and create new ML algorithms. 

Course Content

Dimensionality reduction, and density estimation

  • Principal Component Analysis (PCA), nonlinear dimensionality reduction
  • Basic density estimation
  • Gaussian mixture model

Computational algorithms

  • K-means, spectral clustering
  • Expectation-maximization (EM) algorithm
  • Naïve bayes, logistic regression, Support Vector Machine (SVM)
  • Neural networks
  • Boosting, AdaBoost
  • Tree-based methods, random forests

Underlying concepts

  • Cross-validation, bias-variance tradeoff
  • Basic optimization theory

Practical questions

  • Unsupervised learning, clustering
  • Classification
  • Probability estimation
  • Feature selection
  • Anomaly detection
Requirements & Materials

Prerequisites

RECOMMENDED:

  • Familiarity with probability/statistics and algorithms

REQUIRED:

  • Proficiency in Python or MATLAB programming
  • Strong numerate background

 

Materials

REQUIRED (Student must provide):

  • Internet connection
  • Free software (download and install before taking the course)
  • Laptop or desktop computer (not a tablet)
  • Reference books (Not required)
    • (PRML) C.M. Bishop. Pattern Recognition and Machine Learning
    • (ESL) T. hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Predictions, 2nd ed.
    • (FML) M. Mohri, A. Rostamizadeh, A. Talwalkar. Foundations of Machine Learning, 2nd ed.

PROVIDED (Student will receive):

  • All course lessons, slides, assignments, solutions

Who Should Attend

This course is designed for people who want to gain proficiency in the use of machine learning AI tools as more than black boxes. It is ideal for individuals who want to learn enough underlying methodology to understand how to tune parameters, set objectives, and interpret results, and create their own machine learning algorithms.

Computer science students coding on computers

What You Will Learn

  • Implementation and use of machine learning algorithms
  • Methods, algorithms, theory, and mathematics needed to apply Machine Learning AI methods
  • Utilization of Machine Learning to analyze real data
Analytics professional learning on computer and laptop

How You Will Benefit

  • Understand how Machine Learning works at a detailed level that is relevant to application.
  • Learn how to tune parameters and set objectives to get maximum performance from Machine Learning methods.
  • Determine which Machine Learning approach works best for different datasets.
  • Apply Machine Learning for answering practical questions more effectively and getting beyond a black-box implementation.
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    Taught by Experts in the Field

TRAIN AT YOUR LOCATION

We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
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