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AWS Certified Machine Learning Specialty 2024 - Hands On!

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Stephane Maarek | AWS Certified Cloud Practitioner,Solutions Architect,Developer,Sundog Education by Frank Kane,Frank Kane,Sundog Education Team

11:06:48

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  • 001 Udemy 101.mp4
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  • 002 Course Introduction What to Expect.mp4
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  • 003 Get the Course Materials.mp4
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  • external-links.txt
  • 001 Section Intro Data Engineering.mp4
    00:40
  • 002 Amazon S3 - Overview.mp4
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  • 003 Amazon S3 Storage Classes + Glacier.mp4
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  • 004 Amazon S3 Storage + Glacier - Hands On.mp4
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  • 005 Amazon S3 Lifecycle Rules.mp4
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  • 006 Amazon S3 Lifecycle Rules - Hands On.mp4
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  • 007 Amazon S3 Security.mp4
    08:05
  • 008 Kinesis Data Streams & Kinesis Data Firehose.mp4
    10:52
  • 009 Lab 1.1 - Kinesis Data Firehose.mp4
    05:39
  • 010 Kinesis Data Analytics.mp4
    04:25
  • 011 Lab 1.2 - Kinesis Data Analytics.mp4
    08:16
  • 012 Kinesis Video Streams.mp4
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  • 013 Kinesis ML Summary.mp4
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  • 014 Glue Data Catalog & Crawlers.mp4
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  • 015 Lab 1.3 - Glue Data Catalog.mp4
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  • 016 Glue ETL.mp4
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  • 017 Lab 1.4 - Glue ETL.mp4
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  • 018 Lab 1.5 - Athena.mp4
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  • 019 Lab 1 - Cleanup.mp4
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  • 020 AWS Data Stores in Machine Learning.mp4
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  • 021 AWS Data Pipelines.mp4
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  • 022 AWS Batch.mp4
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  • 023 AWS DMS - Database Migration Services.mp4
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  • 024 AWS Step Functions.mp4
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  • 025 Full Data Engineering Pipelines.mp4
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  • 026 Data Engineering Summary.html
  • 001 Section Intro Data Analysis.mp4
    01:12
  • 002 Python in Data Science and Machine Learning.mp4
    12:08
  • 003 Example Preparing Data for Machine Learning in a Jupyter Notebook.mp4
    10:21
  • 003 deeplearningproject-solution.zip
  • 004 Types of Data.mp4
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  • 005 Data Distributions.mp4
    06:05
  • 006 Time Series Trends and Seasonality.mp4
    03:57
  • 007 Introduction to Amazon Athena.mp4
    05:06
  • 008 Overview of Amazon Quicksight.mp4
    05:59
  • 009 Types of Visualizations, and When to Use Them.mp4
    04:46
  • 010 Elastic MapReduce (EMR) and Hadoop Overview.mp4
    07:14
  • 011 Apache Spark on EMR.mp4
    09:59
  • 012 EMR Notebooks, Security, and Instance Types.mp4
    04:10
  • 013 Feature Engineering and the Curse of Dimensionality.mp4
    06:34
  • 014 Imputing Missing Data.mp4
    08:04
  • 015 Dealing with Unbalanced Data.mp4
    05:35
  • 016 Handling Outliers.mp4
    08:30
  • 017 Binning, Transforming, Encoding, Scaling, and Shuffling.mp4
    07:59
  • 018 Amazon SageMaker Ground Truth and Label Generation.mp4
    05:35
  • 019 Lab Preparing Data for TF-IDF with Spark and EMR, Part 1.mp4
    06:18
  • 020 Lab Preparing Data for TF-IDF with Spark and EMR, Part 2.mp4
    13:36
  • 021 Lab Preparing Data for TF-IDF with Spark and EMR, Part 3.mp4
    13:42
  • 001 Section Intro Modeling.mp4
    01:47
  • 002 Introduction to Deep Learning.mp4
    09:23
  • 003 Activation Functions.mp4
    10:50
  • 004 Convolutional Neural Networks.mp4
    12:07
  • 005 Recurrent Neural Networks.mp4
    10:48
  • 006 Deep Learning on EC2 and EMR.mp4
    01:32
  • 007 Tuning Neural Networks.mp4
    04:48
  • 008 Regularization Techniques for Neural Networks (Dropout, Early Stopping).mp4
    06:41
  • 009 L1 and L2 Regularization.mp4
    03:04
  • 010 Grief with Gradients The Vanishing Gradient problem.mp4
    04:28
  • 011 The Confusion Matrix.mp4
    07:29
  • 012 Precision, Recall, F1, AUC, and more.mp4
    06:59
  • 013 Ensemble Methods Bagging and Boosting.mp4
    03:43
  • 001 Introducing Amazon SageMaker.mp4
    08:06
  • 002 Linear Learner in SageMaker.mp4
    04:59
  • 003 XGBoost in SageMaker.mp4
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  • 004 Seq2Seq in SageMaker.mp4
    04:47
  • 005 DeepAR in SageMaker.mp4
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  • 006 BlazingText in SageMaker.mp4
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  • 007 Object2Vec in SageMaker.mp4
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  • 008 Object Detection in SageMaker.mp4
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  • 009 Image Classification in SageMaker.mp4
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  • 010 Semantic Segmentation in SageMaker.mp4
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  • 011 Random Cut Forest in SageMaker.mp4
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  • 012 Neural Topic Model in SageMaker.mp4
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  • 013 Latent Dirichlet Allocation (LDA) in SageMaker.mp4
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  • 014 K-Nearest-Neighbors (KNN) in SageMaker.mp4
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  • 015 K-Means Clustering in SageMaker.mp4
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  • 016 Principal Component Analysis (PCA) in SageMaker.mp4
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  • 017 Factorization Machines in SageMaker.mp4
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  • 018 IP Insights in SageMaker.mp4
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  • 019 Reinforcement Learning in SageMaker.mp4
    12:23
  • 020 Automatic Model Tuning.mp4
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  • 021 Apache Spark with SageMaker.mp4
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  • 022 SageMaker Studio, and SageMaker Experiments.mp4
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  • 023 SageMaker Debugger.mp4
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  • 024 SageMaker Autopilot AutoML.mp4
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  • 025 SageMaker Model Monitor.mp4
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  • 026 Other recent features (JumpStart, Data Wrangler, Features Store, Edge Manager).mp4
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  • 027 SageMaker Canvas.mp4
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  • 028 SageMaker Training Compiler.mp4
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  • 001 Amazon Comprehend.mp4
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  • 002 Amazon Translate.mp4
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  • 003 Amazon Transcribe.mp4
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  • 004 Amazon Polly.mp4
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  • 005 Amazon Rekognition.mp4
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  • 006 Amazon Forecast.mp4
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  • 007 Amazon Lex.mp4
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  • 008 Amazon Personalize.mp4
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  • 009 Lightning round! TexTract, DeepLens, DeepRacher, Lookout, and Monitron.mp4
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  • 010 TorchServe, AWS Neuron, and AWS Panorama.mp4
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  • 011 Deep Composer, Fraud Detection, CodeGuru, and Contact Lens.mp4
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  • 012 Amazon Kendra and Amazon Augmented AI (A2I).mp4
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  • 001 Putting them All Together.mp4
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  • 002 Lab Tuning a Convolutional Neural Network on EC2, Part 1.mp4
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  • 003 Lab Tuning a Convolutional Neural Network on EC2, Part 2.mp4
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  • 004 Lab Tuning a Convolutional Neural Network on EC2, Part 3.mp4
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  • 001 Section Intro Machine Learning Implementation and Operations.mp4
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  • 002 SageMakers Inner Details and Production Variants.mp4
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  • 003 SageMaker On the Edge SageMaker Neo and IoT Greengrass.mp4
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  • 004 SageMaker Security Encryption at Rest and In Transit.mp4
    04:31
  • 005 SageMaker Security VPCs, IAM, Logging, and Monitoring.mp4
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  • 006 SageMaker Resource Management Instance Types and Spot Training.mp4
    03:35
  • 007 SageMaker Resource Management Elastic Inference, Automatic Scaling, AZs.mp4
    04:34
  • 008 SageMaker Serverless Inference and Inference Recommender.mp4
    04:16
  • 009 SageMaker Inference Pipelines.mp4
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  • 010 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1.mp4
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  • 011 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2.mp4
    10:33
  • 012 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3.mp4
    12:21
  • 001 Section Intro Wrapping Up.mp4
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  • 002 More Preparation Resources.mp4
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  • 003 Test-Taking Strategies, and What to Expect.mp4
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  • 004 You Made It!.mp4
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  • 005 Save 50% on your AWS Exam Cost!.mp4
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  • 006 Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only.mp4
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  • 001 THANK YOU!.mp4
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  • 002 Bonus Lecture.html
    •

    Description

    AWS machine learning certification preparation - learn SageMaker, generative AI, data engineering, modeling & more

    What You'll Learn?

    • What to expect on the AWS Certified Machine Learning Specialty exam
    • Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
    • Feature engineering techniques, including imputation, outliers, binning, and normalization
    • High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
    • Data engineering with S3, Glue, Kinesis, and DynamoDB
    • Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
    • Deep learning and hyperparameter tuning of deep neural networks
    • Automatic model tuning and operations with SageMaker
    • L1 and L2 regularization
    • Applying security best practices to machine learning pipelines

    Who is this for?

  • Individuals performing a development or data science role seeking certification in machine learning and AWS.

    • What You Need to Know?

  • Associate-level knowledge of AWS services such as EC2
  • Some existing familiarity with machine learning
  • An AWS account is needed to perform the hands-on lab exercises

    • More details

    Description

    Updated for the latest SageMaker features, Generative AI (GPT), and new AWS ML Services. Happy learning!

    Nervous about passing the AWS Certified Machine Learning - Specialty exam (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one.

    This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.

    In addition to the 11-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering.

    This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:


    • How generative AI and large language models (LLM's) work, including the Transformer architecture (GPT) and attention-based neural networks (masked self-attention)

    • Amazon's newest generative AI services: Bedrock, SageMaker JumpStart for Generative AI, CodeWhisperer, and SageMaker Foundation Models

    • S3 data lakes

    • AWS Glue and Glue ETL

    • Kinesis data streams, firehose, and video streams

    • DynamoDB

    • Data Pipelines, AWS Batch, and Step Functions

    • Using scikit_learn

    • Data science basics

    • Athena and Quicksight

    • Elastic MapReduce (EMR)

    • Apache Spark and MLLib

    • Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)

    • Ground Truth

    • Deep Learning basics

    • Tuning neural networks and avoiding overfitting

    • Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger.

    • Regularization techniques

    • Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)

    • High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more

    • Building recommender systems with Amazon Personalize

    • Monitoring industrial equipment with Lookout and Monitron

    • Security best practices with machine learning on AWS

    Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.

    If there's a more comprehensive prep course for the AWS Certified Machine Learning - Specialty exam, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.


    Instructor

    My name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.

    I have already taught 1,500,000+ students and gotten 500,000+ reviews throughout my career in designing and delivering these certifications and courses!

    With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Machine Learning Professional. So, let’s kick start the course! You are in good hands!


    Instructor

    Hey, I'm Frank Kane, and I'm also instructing this course. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, where my specialty was recommender systems and machine learning. As an instructor, I'm best known for my top-selling courses in "big data", data analytics, machine learning, Apache Spark, system design, technical management and career growth, and Elasticsearch.

    I've been teaching on Udemy since 2015, where I've reached over 700,00 students all around the world!

    I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!


    This course also comes with:

    • Lifetime access to all future updates

    • A responsive instructor in the Q&A Section

    • Udemy Certificate of Completion Ready for Download

    • A 30 Day "No Questions Asked" Money Back Guarantee!

    Join us in this course if you want to prepare for the AWS Machine Learning Certification and master the AWS platform!

    Who this course is for:

    • Individuals performing a development or data science role seeking certification in machine learning and AWS.

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    Machine Learning

    Amazon AWS

    Stephane Maarek | AWS Certified Cloud Practitioner,Solutions Architect,Developer
    Stephane Maarek | AWS Certified Cloud Practitioner,Solutions Architect,Developer
    Instructor's Courses
    Stephane is a solutions architect, consultant and software developer that has a particular interest in all things related to Big Data, Cloud & API. He's also a many-times best seller instructor on Udemy for his courses in AWS and Apache Kafka.[See FAQ below to see in which order you can take my courses]Stéphane is recognized as an AWS Hero and is an AWS Certified Solutions Architect Professional & AWS Certified DevOps Professional. He loves to teach people how to use the AWS properly, to get them ready for their AWS certifications, and most importantly for the real world. He also loves Apache Kafka. He sits on the 2019 Program Committee organizing the Kafka Summit in New York, London and San Francisco. He is also an active member of the Apache Kafka community, authoring blogs on Medium and a guest blog for Confluent.  During his spare time he enjoys cooking, practicing yoga, surfing, watching TV shows, and traveling to awesome destinations!FAQ: In which order should you learn?...AWS Cloud: Start with AWS Certified Solutions Architect Associate, then move on to AWS Certified Developer Associate and then AWS Certified SysOps Administrator. Afterwards you can either do AWS Certified Solutions Architect Professional or AWS Certified DevOps Professional, or a specialty certification of your choosing.Apache Kafka: Start with Apache Kafka for Beginners, then you can learn Connect, Streams and Schema Registry if you're a developer, and Setup and Monitoring courses if you're an admin. Both tracks are needed to pass the Confluent Kafka certification.
    Sundog Education by Frank Kane
    Sundog Education by Frank Kane
    Instructor's Courses
    Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
    Frank Kane
    Frank Kane
    Instructor's Courses
    Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.
    Sundog Education Team
    Sundog Education Team
    Instructor's Courses
    Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Sundog Education CEO, Frank Kane, spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.
    Udemy
    Udemy
    View courses Udemy
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 125
    • duration 11:06:48
    • English subtitles has
    • Release Date 2024/02/25

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