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    AWS Certified Machine Learning Specialty (MLS-C01)

    Posted By: BlackDove
    AWS Certified Machine Learning Specialty (MLS-C01)

    AWS Certified Machine Learning Specialty (MLS-C01)
    Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 5.5 GB | Duration: 233 lectures • 17h 35m


    Hands on AWS ML SageMaker Course with Practice Test. Join Live Study Group Q&A!

    What you'll learn
    You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
    AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
    Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
    Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)
    Zero Downtime Model Deployment
    How to Integrate and Invoke ML from your Application
    Automated Hyperparameter Tuning

    Requirements
    Familiarity with Python
    AWS Account - I will walk through steps to setup one
    Basic knowledge of Pandas, Numpy, Matplotlib
    Be an active learner and use course discussion forum if you need help - Please don't put help needed items in course review
    Description
    Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

    *** NEW Labs - A/B Testing, Multi-model endpoints ***

    *** NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness ***

    *** New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime ***

    *** Lab notebook now use spot-training as the default option. Save over 60% in training costs ***

    *** NEW: Nuts and Bolts of Optimization, quizzes ***

    *** All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***

    *** Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest. With labs. ***

    *** Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

    *** Timed Practice Test and additional lectures for Exam Preparation added

    Welcome to AWS Machine Learning Specialty Course!

    I am Chandra Lingam, and I am your instructor

    In this course, you will gain first-hand SageMaker experience with many hands-on labs that demonstrates specific concepts

    We start with how to set up your SageMaker environment

    If you are new to ML, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model

    These topics are very important for an ML practitioner as well as for the certification exam

    SageMaker uses containers to wrap your favorite algorithms and frameworks such as Pytorch, and TensorFlow

    The advantage of a container-based approach is it provides a standard interface to build and deploy your models

    It is also straightforward to convert your model into a production application

    In a series of concise labs, you will in fact train, deploy, and invoke your first SageMaker model

    Like any other software project, ML Solution also requires continuous improvement

    We look at how to safely incorporate new changes in a production system, perform A/B testing, and even rollback changes when necessary

    All with zero downtime to your application

    We then look at emerging social trends on the fairness of Machine learning and AI systems.

    What will you do if your users accuse your model as racially biased or gender-biased? How will you handle it?

    In this section, we look at the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them

    We then look at Cloud security – how to protect your data and model from unauthorized use

    You will also learn about recommender systems to incorporate features such as movie and product recommendation

    The algorithms that you learn in the course are state of the art, and tuning them for your dataset is especially challenging

    So, we look at how to tune your model with automated tools

    You will gain experience in time series forecasting

    Anomaly detection and building custom deep learning models

    With the knowledge, you gain here and the included high-quality practice exam, you will easily achieve the certification!

    And something unique that I offer my students is a weekly study group meeting to discuss and clarify any questions

    I am looking forward to seeing you!

    Thank you!

    Who this course is for
    This course is designed for anyone who is interested in AWS cloud based machine learning and data science
    AWS Certified Machine Learning - Specialty Preparation