Aws Machine Learning




Machine Learning Training Insitute


All Job Seekers


Both Classroom and Online Classes


Week Days and Week Ends

Duration :

45 Days

Machine Learning Objectives

•Build and deploy web applications Machine Learning.
•How to create delete and view Machine Learning.
•Learn Machine Learning from scratch. Code like a PRO
•Learn End to End Machine Learning complete ground up
•You will know how to design Machine Learning from scratch.
•Learn Machine Learningat a minimal cost and enjoy the instructor support.
•Components states props how to pass variables between components in Machine Learning.
•Learn all the topics from Machine Learning from the basics to advanced topics
•Get to know tips and tricks to work more quickly and effectively in Machine Learning.

aws machine learning Course Features

•Free Aptitude classes & Mock interviews
• First step to landing an entry-level job
• Helps you stand out in a competitive market
•The courses range from basic to advanced level
• Greater productivity and increased workforce morale
•Courseware includes reference material to maximize learning.
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Machine Learning

•.Net,, Application Support, Manual Testing, Business Analyst, Angularjs, Angular6, Angular7, Node.js, Mean Stack, Mern, Dot Net Developer, Fresher
•HR, HR Manager, Human Resource Manager, HR Generalist, Cognos, BI Developer, OBIEE, Tableau, qlikview, Data Modeling, Dimensional Modeling,
•Java, J2EE, Machine Learning, Image Processing, UI Design, UX Developer, C++, Python, Perl
•Python, React, Javascript, Html, Css, Web Technologies, Front End Developer, Backend Developer, Mysql, Mongodb
•Software Development, Big Data, Hadoop, Spark, Hive, Oozie, Big Data Analytics, Java, Python, R, Cloud, Data Quality, Scala, Nosql, Sql Database, Core Java


•Get the Course Materials
•Data Engineering
•Section Intro: Data Engineering
•Amazon S3 – Overview
•Amazon S3 – Storage Tiers & Lifecycle Rules
•Amazon S3 Security
•Kinesis Data Streams & Kinesis Data Firehose
•Lab 1.1 – Kinesis Data Firehose
•Kinesis Data Analytics
•Lab 1.2 – Kinesis Data Analytics
•Kinesis Video Streams
•Kinesis ML Summary
•Glue Data Catalog & Crawlers
•Lab 1.3 – Glue Data Catalog
•Glue ETL
•Lab 1.4 – Glue ETL
•Lab 1.5 – Athena
•Lab 1 – Cleanup
•AWS Data Stores in Machine Learning
•AWS Data Pipelines
•AWS Batch
•AWS DMS – Database Migration Services
•AWS Step Functions
•Full Data Engineering Pipelines
•Data Engineering Summary
•Quiz: Data Engineering
•Exploratory Data Analysis
•Section Intro: Data Analysis
•Python in Data Science and Machine Learning
•Example: Preparing Data for Machine Learning in a Jupyter Notebook.
•Types of Data
•Data Distributions
•Time Series: Trends and Seasonality
•Introduction to Amazon Athena
•Overview of Amazon Quicksight
•Types of Visualizations, and When to Use Them.
•Elastic MapReduce (EMR) and Hadoop Overview
•Apache Spark on EMR
•EMR Notebooks, Security, and Instance Types
•Feature Engineering and the Curse of Dimensionality
•Imputing Missing Data
•Dealing with Unbalanced Data
•Handling Outliers
•Binning, Transforming, Encoding, Scaling, and Shuffling
•Amazon SageMaker Ground Truth and Label Generation
•Lab: Preparing Data for TF-IDF with Spark and EMR,
•Quiz: Exploratory Data Analysis
•Section Intro: Modeling
•Introduction to Deep Learning
•Activation Functions
•Convolutional Neural Networks
•Recurrent Neural Networks
•Deep Learning on EC2 and EMR
•Tuning Neural Networks
•Regularization Techniques for Neural Networks (Dropout, Early Stopping)
•Grief with Gradients: The Vanishing Gradient problem
•L1 and L2 Regularization
•The Confusion Matrix
•Precision, Recall, F1, AUC, and more
•Ensemble Methods: Bagging and Boosting
•Introducing Amazon SageMaker
•Linear Learner in SageMaker
•XGBoost in SageMaker
•Seq2Seq in SageMaker
•DeepAR in SageMaker
•BlazingText in SageMaker
•Object2Vec in SageMaker
•Object Detection in SageMaker
•Image Classification in SageMaker
•Semantic Segmentation in SageMaker
•Random Cut Forest in SageMaker
•Neural Topic Model in SageMaker
•Latent Dirichlet Allocation (LDA) in SageMaker
•K-Nearest-Neighbors (KNN) in SageMaker
•K-Means Clustering in SageMaker
•Principal Component Analysis (PCA) in SageMaker
•Factorization Machines in SageMaker
•IP Insights in SageMaker
•Reinforcement Learning in SageMaker
•Automatic Model Tuning
•Apache Spark with SageMaker
•SageMaker Studio, and new SageMaker features for 2020
•Amazon Comprehend
•Amazon Translate
•Amazon Transcribe
•Amazon Polly
•Amazon Rekognition
•Amazon Forecast
•Amazon Lex
•The Best of the Rest: Other High-Level AWS Machine Learning Services
•ML Services for 2020
•Putting them All Together
•Lab: Tuning a Convolutional Neural Network on EC2,
•Quiz: Modeling
•ML Implementation and Operations
•Section Intro: Machine Learning Implementation and Operations
•SageMaker’s Inner Details and Production Variants
•SageMaker On the Edge: SageMaker Neo and IoT Greengrass
•SageMaker Security: Encryption at Rest and In Transit
•SageMaker Security: VPC’s, IAM, Logging, and Monitoring
•SageMaker Resource Management: Instance Types and Spot Training
•SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ’s
•SageMaker Inference Pipelines
•Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker –
•Quiz: ML Implementation and Operations
•Wrapping Up
•Section Intro: Wrapping Up
•More Preparation Resources
•Test-Taking Strategies, and What to Expect
•You Made It!
•Save 50% on your AWS Exam Cost!
•Get an Extra 30 Minutes on your AWS Exam – Non Native English Speakers only
•Practice Exams
•Warmup Test: Quick Assessment
•Bonus Lecture: Get the Full 3-Hour Practice Exam