Introduction to Artificial Intelligence (AI) & Machine Learning (AI & ML JumpStart) is a three-day, foundation-level, hands-on course that explores the fast-changing field of artificial intelligence (AI). programming, logic, search, machine learning, and natural language understanding. Students will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.
In this course, we will cut through the math and learn exactly how machine learning algorithms work. Although there is clearly a requirement for the students to have an aptitude for math, this course is about focusing on the algorithms that will be used to create machine learning models. Using clear explanations, simple pure Python code (no libraries!) and step-by-step labs, you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.
This course presents a wide variety of related technologies, concepts and skills in a fast-paced, hands-on format, providing students with a solid foundation for understanding and getting a jumpstart into working with AI and machine learning. Each topic area presents a specific challenge area, current progress, and approaches to the presented problem. Attendees will exit the course with practical understanding of related core skills, methods and algorithms, and be prepared for continued learning in next-level, more advanced follow on courses that dive deeper into specific skillsets or tools.
What You'll Learn
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. Students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations; practical application of these methods in a machine learning environment; and practical use cases and limitations of algorithms.
Working in a hands-on learning environment led by our expert practitioner, attendees will explore:
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, deep learning, data science / big data, programming (Python/R/Scala/Java) and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
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. Students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.
Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations; practical application of these methods in a machine learning environment; and practical use cases and limitations of algorithms.
Working in a hands-on learning environment led by our expert practitioner, attendees will explore:
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, deep learning, data science / big data, programming (Python/R/Scala/Java) and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
Students attending this class should have a grounding in Enterprise computing. Students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI. This course is ideally suited for a wide variety of technical learners who need an introduction to the core skills, concepts and technologies related to AI programming and machine learning. Attendees might include:
Pre-Requisites: Students should have attended or have incoming skills equivalent to those in this course:
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
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. Getting Started
2. Statistics and Probability Refresher, and Python Practice
3. Matplotlib and Advanced Probability Concepts
4. Algorithm Overview
5. Predictive Models
6. Applied Machine Learning with Python
7. Recommender Systems
8. More Applied Machine Learning Techniques
9. Dealing with Data in the Real World
10. Apache Spark Basics | Machine Learning on Big Data
11. Testing and Experimental Design
12. GUIs and REST
13. What the Future Holds
Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.
Lab Setup Made Simple. All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.
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Please see the current upcoming available open enrollment course dates posted below. Please feel free to Register Online below, or call 844-475-4559 toll free to connect with our Registrar for assistance. If you need additional date options, please contact us for scheduling.
Course Title | Days | Date | Time | Price | |
---|---|---|---|---|---|
Introduction to AI, AI Programming and Machine Learning (AI / ML JumpStart) (TTML5503) | 3 Days | Jul 19 to Jul 21 | 10:00 AM to 06:00 PM EST | $1,995.00 | Enroll |
Introduction to AI, AI Programming and Machine Learning (AI / ML JumpStart) (TTML5503) | 3 Days | Sep 13 to Sep 15 | 10:00 AM to 06:00 PM EST | $1,995.00 | Enroll |
Introduction to AI, AI Programming and Machine Learning (AI / ML JumpStart) (TTML5503) | 3 Days | Nov 15 to Nov 17 | 10:00 AM to 06:00 PM EST | $1,995.00 | Enroll |
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