Machine Learning Essentials with Python and Spark is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern ML at scale practices, leveraging Python and Spark. This course is geared for attendees new to machine learning who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use cases.
This program provides a structured, engaging learning experience for students to progressively attain the technical skills they require to engage in meaningful machine learning projects and activities, right after the training ends. Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
This course is geared for experienced, intermediate-skilled developers or others (with prior Python experience) intending to start using learning about and working with basic machine learning algorithms and concepts. Attendees should be comfortable working with Python programming. Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code.
Pre-Requisites: 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. (NOTE: This course is also offered in R – please inquire for details)
· Good foundational mathematics in Linear Algebra and Probability
· Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
Time Permitting: Capstone Project
Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.
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