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About Psitron Technologies:

Psitron Technologies is an IoT and AI Company. 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?

This is the best time to learn machine learning as the trends in the market suggest. The global machine learning market is estimated at US dollar 8.43 billion in 2019 and is expected to reach 117 billion by 2027, at a CAGR of 39.2%. Thus, job opportunities in this sector will grow with a boom in the coming years.

AI and machine learning are not only used in machine learning applications but also in the Internet of things, like self-driving cars, smart homes, digital assistants, etc. In fact, during COVID-19, statistical machine learning played a significant role in generating advanced models for predicting virus spread and aiding in the management of the pandemic across the world. Machine learning in finance has also secured a respectable place among business leaders using technology for generating automatic models for stock management.

Artificial intelligence can no longer be considered a technology of the future it is already shaping our everyday lives. There is no doubt that we are entering the age of artificial intelligence.

Schedule:

Date & Time:

Note: All sessions are on weekends

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
AWS Containers
AWS Serverless

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
AWS Panorama
Amazon Monitron
Amazon HealthLake
Amazon Lookout for Vision
Amazon Lookout for Equipment
Amazon Lookout for Metrics

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. Machine Learning Operations(MLOps)

MLOps

What is MLOps?
MLOps Motivation: High-level view
MLOps challenges
MLOps challenges similar to DevOps
Automated ML pipelines vs CI/CD ML pipelines
Amazon SageMaker Pipelines [Hands-on]
AWS SageMaker studio [Hands-on]

8. 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

Program price Structure:

Program price includes:

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

Group Discount:

Certification:

Participants will be certified by Psitron Technologies Pvt. Ltd.

Rules and Requirements:

Every participant must have one laptop of their own.
Required Software installation assistance will be provided by 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.

AWS Machine Learning Specialty Certification Course

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Sarath Kumar

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