AWS Machine Learning with Python, Part 2 of 4: Fundamentals

Course description

This course seeks to equip a student with a solid understanding of machine learning fundamentals as well as using AWS Rekognition. Topics covered in this course are Machine Learning Basics, Unsupervised learning, neural network techniques and an introduction to reinforcement learning.

Each LearnNowOnline training course is made up of Modules (typically an hour in length). Within each module there are Topics (typically 15-30 minutes each) and Subtopics (typically 2-5 minutes each). There is a Post Exam for each Module that must be passed with a score of 70% or higher to successfully and fully complete the course.

Prerequisites

the following Python courses are recommended unless the user knows Python Python 3, Part 1 of 6: Getting Started Python 3, Part 2 of 6: Statements Python 3, Part 3 of 6: Data Python 3, Part 4 of 6: Functions and Classes Python 3, Part 5 of 6: Programming Tools Python 3, Part 6 of 6: Modules, JSON, and Algorithms • Basic knowledge of programming concepts desirable • Valid AWS account requires a credit card • AWS will bill you for using Machine Learning resources such as AWS Rekognition. It is not free, and you are solely responsible for all AWS billing • Knowledge of cloud computing concepts is desirable

Meet the expert

Syed Raza

Syed Raza is an IT Enterprise Solutions, Senior Project Architect and Manager.  He has over 20 years experience in  DevOps, Agile, Lean SixSigma, ITIL, ITSM along with AWS, Azure, Google Cloud enterprise solutions including Python, Java, JavaScript, React JS, GCP, Kubernetes, Docker as well as Artificial intelligence.

Video Runtime

75 Minutes

Time to complete

132 Minutes

Course Outline

Fundamentals

Machine Learning Basics (16:57)

• Introduction (00:13)
• Basics of Machine Learning (07:50)
• Supervised Learning (01:41)
• Learning Types (07:04)
• Summary (00:08)

Unsupervised Learning (24:11)

• Introduction (00:08)
• Unsupervised Learning (03:15)
• Hierarchical clustering (03:59)
• Dimensionality Reduction (02:40)
• Deep Neural Networks (05:50)
• Neural Network (03:49)
• Deep Nueral Network (04:19)
• Summary (00:08)

Neural Network Techniques (21:06)

• Introduction (00:08)
• Neural Network Techniques (07:52)
• Applications (06:22)
• Generative Adversarial Nettwork (06:35)
• Summary (00:08)

Intro to Reinforcement Learning (13:23)

• Introduction (00:08)
• Intro to Reinforcement Learning (06:19)
• Reinforcement Learning in Action (03:23)
• Implement Algorith (03:25)
• Summary (00:08)