Introduction to Artificial Intelligence (AI), AI Programming, and Machine Learning (ML)

This foundation-level hands-on course explores the field of artificial intelligence (AI), programming, logic, search, machine learning (ML), and natural language understanding. You’ll learn current AI and ML methods, tools, techniques, and their application to computational problems.

In this course, we’ll cut through the math and you’ll learn exactly how machine learning algorithms work. We’ll focus on the algorithms used to create machine learning models. Using clear explanations, simple Python code (no libraries), and step-by-step labs, you’ll discover how to load and prepare data, evaluate your models, and implement a suite of linear and nonlinear algorithms along with assembling algorithms from scratch. You’ll also learn about algorithm applicability along with their limitations and practical use cases.

This course presents a wide variety of related technologies, concepts, and skills in a fast-paced, hands-on format. This provides you with a solid foundation for understanding and getting a jumpstart into working with artificial intelligence and machine learning.

    Dec 1 2020

    December 1 - 3, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live

    Date: 12/01/2020 - 12/03/2020 (Tuesday - Thursday) | 10:00 AM - 6:00 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Introduction to Artificial Intelligence (AI), AI Programming, and Machine Learning (ML)

    December 1 - 3, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live


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Join an engaging hands-on learning environment, where you’ll explore:

  • Getting Started with Python and Jupyter
  • Statistics and Probability Refresher and Python Practice
  • Matplotlib and Advanced Probability Concepts
  • Algorithm Overview
  • Predictive Models
  • Applied Machine Learning
  • Recommender Systems
  • Dealing with Data in the Real World
  • Machine Learning on Big Data (with Apache Spark)
  • Testing and Experimental Design
  • GUIs and REST: Build a UI and REST API for your Models

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:

  • Basic Python skills
  • A grounding in enterprise computing
  • Be familiar with enterprise IT
  • Have a general (high-level) understanding of systems architecture
  • Knowledge of business drivers that might be able to take advantage of applying AI
  • Good foundational mathematics in linear algebra and probability
  • Basic Linux skills
  • Familiarity with command line options such as ls, cd, cp, and su

Business Analysts, Data Analysts, Developers, Administrators, Architects, Managers, and others new to AI and ML who want to understand the core skills and how to put them into practice.

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