AWS Machine Learning Specialty Course

AWS Machine Learning Specialty Course

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February 13 | 1PM - March 7 | 5PM

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About the Event

About Psitron Technologies:

Psitron Technologies is an IoT and AI Startups. Our mission at Psitron is to connect the world with innovative technologies. Psitron has responsibility for developing innovative innovations for addressing current problems in industries, especially focusing on industry 4.0 solutions.


Do you know?

According to a Gartner report, Artificial Intelligence (AI) is estimated to pave the way for close to 2.3 million opportunities in upcoming years. And if you look at a report from Indeed, vacancies in the realm of artificial intelligence has doubled over the last three years. Artificial intelligence can no longer be considered a technology of the future it is already shaping our everyday lives. Here is our guide to understanding the minds of machines.


While today's AI systems still fall short of that goal, they are starting to perform as well as, and sometimes better than, their creators at certain tasks. Thanks to new techniques that allow machines to learn from enormous sets of data, AI has taken massive leaps forward.


 AI is starting to move out of research labs and into the real world. It is having an impact on our lives. There can be little doubt that we are entering the age of artificial intelligence.


About Course:

Psitron Technologies Pvt. Ltd presents a complete course on AWS Machine Learning Specialty certification. In this complete course, participants will be able to experience and explain the following things.


What is Machine Learning?

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.


Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.


Duration:

8 consecutive days with 4 hours session each day, for a total of 32 hours consisting of theory and hands-on practical sessions ,20 hours Programming Assignments.


Platform: Amazon Chime (ONLINE)


Schedule:

Note: All sessions are on Weekends


Date & Time:


  • 13th February, 1 PM to 5 PM
  • 14th February, 1 PM to 5 PM
  • 20th February, 1 PM to 5 PM
  • 21st February, 1 PM to 5 PM
  • 27th February, 1 PM to 5 PM
  • 28th February, 1 PM to 5 PM
  • 6th March, 1 PM to 5 PM
  • 7th March, 1 PM to 5 PM

Course Curriculum:


1.Refreshing Basics 

Getting Started with Python basics


  • Python Basic [Hands-on]
  • Introducing the Pandas Library [Hands-on]

Statistics & Probability 


  • Data Types
  • Mean, Mode & Median [Hands-on]
  • Probability Density Function, Probability Mass Function
  • Common Data Distributions
  • Percentiles and Moments [Hands-on]
  • Variation and Standard Deviation [Hands-on]
  • Conditional Probability
  • Using matplotlib & Seaborn [Hands-on]
  • The Bayes’s theorem
  • Linear Regression [Hands-on]
  • Polynomial Regression & Multiple Regression [Hands-on]

Data Engineering Basic


  • Bias or Variance Trade-off
  • Data Cleaning & Normalization
  • Normalizing numerical data
  • K-Fold cross-validation to avoid overfitting [Hands-on]
  • Feature Engineering and the Curse of Dimensionality
  • Techniques for Imputation Missing Data
  • Oversampling, Under sampling, and SMOTE
  • Binning, Transforming, Encoding, Scaling, and Shuffling
  • Dealing with Unbalanced data
  • Handling outliers [Hands-on]

2.Data Engineering in AWS

  • Introduction to Data Engineering
  • Amazon S3 [Hands-on]
  • Amazon S3 - Storage classes & Lifecycle Rules Amazon S3 Security
  • Amazon S3 Security
  • Glue Data Catalog & Crawlers [Hands-on]
  • Glue ETL [Hands-on]
  • Kinesis Data Streams & Kinesis Data Firehose [Hands-on]
  • Kinesis Data Analytics [Hands-on]
  • Kinesis Video Streams
  • Kinesis ML Summary
  • Introduction Athena
  • AWS Data Stores in Machine Learning
  • AWS Data Pipelines
  • AWS Batch
  • AWS DMS - Database Migration Services
  • AWS Step Functions
  • Full Data Engineering Pipelines

 3.Data Analysis in AWS

  • Introduction Data Analysis
  • Preparing Data for Machine Learning in a Jupyter Notebook.
  • Time Series-Trends and Seasonality
  • Amazon Athena [Hands-on]
  • Overview of Amazon Quicksight [Hands-on]
  • Types of Visualizations, and When to Use Them.
  • Hadoop Overview & Elastic MapReduce (EMR) [Hands-on]
  • Apache Spark on EMR [Hands-on]
  • EMR Notebooks, Security, and Instance Types [Hands-on]

4.Modeling in AWS

  • Introduction to 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

5. Artificial Intelligence in AWS

  • Amazon Augmented AI
  • Amazon CodeGuru
  • Amazon Comprehend
  • Amazon Forecast
  • Amazon Fraud Detector
  • Amazon Kendra
  • Amazon Lex [Hands-on]
  • Amazon Personalize
  • Amazon Polly [Hands-on]
  • Amazon Rekognition [Hands-on]
  • Amazon Textract
  • Amazon Transcribe
  • Amazon Translate [Hands-on]
  • AWS DeepComposer
  • AWS DeepLens
  • AWS DeepRacer

6.Machine Learning in SageMaker


Introduction to SageMaker


  • Understanding Machine Learning Pipeline
  • Why SageMaker ?
  • SageMaker for Machine Learning

SageMaker Setup


  • AWS S3 bucket creation
  • Notebook creation
  • Data collection, transformation & upload to S3
  • Model Selection & Training
  • Model Deployment
  • Model Validation

SageMaker Built-in Algorithms


  • BlazingText
  • DeepAR Forecasting
  • Factorization Machines
  • Image Classification Algorithm [Hands-on]
  • IP Insights
  • K-Means Algorithm
  • K-Nearest Neighbors (k-NN) Algorithm
  • Latent Dirichlet Allocation (LDA)
  • Linear learner algorithm [Hands-on]
  • Neural Topic Model (NTM) Algorithm
  • Object2Vec
  • Object Detection Algorithm
  • Principal Component Analysis (PCA) Algorithm
  • Random Cut Forest (RCF) Algorithm [Hands-on]
  • Semantic Segmentation
  • Sequence to Sequence (seq2seq)
  • XGBoost Algorithm

Model Training & Tuning


  • Monitor & Analyze Training jobs
  • Incremental Training
  • Hyperparameter Tuning

Model Deployment


  • Interface Pipeline
  • Train once & Run Anywhere using Neo
  • Elastic Interface
  • Automatic Scaling
  • Standard Practices

Using Machine Learning Frameworks with SageMaker


  • Apache Spark with Amazon SageMaker
  • TensorFlow with Amazon SageMaker [Hands-on]
  • Apache MXNet with Amazon SageMaker
  • Scikit-learn with Amazon SageMaker
  • PyTorch with Amazon SageMaker

Ground Truth using SageMaker


  • Data Labelling
  • Input & Output data to Ground Truth
  • Workforce for labelling - public, private, vendor

7.Monitoring & Watching


Monitoring & Watching


  • Monitoring with CloudWatch [Hands-on]
  • Logging with CloudWatch [Hands-on]
  • Logging in SageMaker API Calls with AWS CloudTrail [Hands-on]

Using SageMaker SDK


  • Understanding boto3
  • Actions - Common APIs
  • Understanding SageMaker Endpoint API

Total Price: Rs. 9999 Only


Program price includes:

  • 32hrs of Training + 20hrs Hands-On
  • Service tax and VAT
  • Certification

Group Discount:

Register as a group of 2 or more and get Rs.1000/- off per person on registrations.


Certification:

Participants will be certified by Psitron Technologies Pvt. Ltd.


Rules and Requirements:

  • Every participant must have one laptop of their own.
  • Required Software's installation assistance will be provided from Psitron

For more information contact.

  • Sarathkumar.C
  • Founder & CEO
  • Psitron Technologies Pvt. Ltd.
  • +918940876397 / +918778033930
  • sarath@psitrontech.com
  • www.psitrontech.com

P.S. - Today, don't play it safe, play it smart.

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