AWS Machine Learning Specialty Course

Psitron Technologies is an IoT and AI startup. 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.

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 are going to 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 had played a significant role in generating advanced models for predicting virus spread, and aided in the management of the pandemic across the world. Machine learning in finance has also secured a respectable place among the business leaders using the 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.

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.

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.

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.

• Python Basic [Hands-on]

• Introducing the Pandas Library [Hands-on]

• 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]

• 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, Undersampling, and SMOTE

• Binning, Transforming, Encoding, Scaling, and Shuffling

• Dealing with Unbalanced data

• Handling outliers [Hands-on]

• 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

• 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]

• 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

• 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

• Understanding Machine Learning Pipeline

• Why SageMaker?

• SageMaker for Machine Learning

• AWS S3 bucket creation

• Notebook creation

• Data collection, transformation & upload to S3

• Model Selection & Training

• Model Deployment

• Model Validation

• 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

• Monitor & Analyze Training jobs

• Incremental Training

• Hyperparameter Tuning

• Interface Pipeline

• Train once & Run Anywhere using Neo

• Elastic Interface

• Automatic Scaling

• Standard Practices

• Data Labelling

• Input & Output data to Ground Truth

• Workforce for labelling - public, private, vendor

• Monitoring with CloudWatch [Hands-on]

• Logging with CloudWatch [Hands-on]

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

• Understanding boto3

• Actions - Common APIs

• Understanding SageMaker Endpoint API

• 32hrs of Training + 20hrs Hands-On

• Service tax and VAT

• Certification

Register as a group of 2 or more and get Rs.1500/- off per person on registrations.

Participants will be certified by Psitron Technologies Pvt. Ltd.

• Every participant must have one laptop of their own.

• Required Software's installation assistance will be provided from Psitron

• Sarathkumar.C

• Founder & CEO

• Psitron Technologies Pvt. Ltd.

• +918940876397 / +918778033930

• sarath@psitrontech.com

• www.psitrontech.com