AWS Machine Learning Specialty Course

Data Scientist | Founder- Psitron Technologies |Educator & MentorEnabler of Applied Analytics, Data Scientist, Artificial Intelligence, and Deep Learning,MLOps for real business problemsEffective manager, Mentor, trainer, problem solver, and proactive contributor with excellent communication, interpersonal skills, and exposure to managing task forces with result-oriented achievements and project delivery

SARATH KUMAR

Complete Hands-on AWS Machine Learning Specialty Course

<h2>About Psitron Technologies:</h2>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.<h2>Do you know?</h2>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.<h2>About Course:</h2>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.<h2>What is Machine Learning?</h2>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.<h2>Duration:</h2>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.<h2>Schedule:</h2>Note: All sessions are on Weekends<h3>Date &amp; Time (IST):</h3>22nd Jan, 7 PM to 11 PM23rd Jan, 7 PM to 11 PM29th Jan, 7 PM to 11 PM30th Jan, 7 PM to 11 PM5th Feb, 7 PM to 11 PM6th Feb, 7 PM to 11 PM12th Feb, 7 PM to 11 PM13th Feb, 7 PM to 11 PM<h2>Course Curriculum:</h2><h3>Download Course Curriculum: <a href="https://bit.ly/AWSMLcurriculum" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">CLICK HERE</a></h3><h3>1. Refreshing Basics </h3>Getting Started with Python basics<ul><li>Python Basic [Hands-on]</li><li>Introducing the Pandas Library [Hands-on]</li></ul>Statistics &amp; Probability<ul><li>Data Types</li><li>Mean, Mode &amp; Median [Hands-on]</li><li>Probability Density Function, Probability Mass Function</li><li>Common Data Distributions</li><li>Percentiles and Moments [Hands-on]</li><li>Variation and Standard Deviation [Hands-on]</li><li>Conditional Probability</li><li>Using matplotlib &amp; Seaborn [Hands-on]</li><li>The Bayes’s theorem</li><li>Linear Regression [Hands-on]</li><li>Polynomial Regression &amp; Multiple Regression [Hands-on]</li></ul>Data Engineering Basic<ul><li>Bias or Variance Trade-off</li><li>Data Cleaning &amp; Normalization</li><li>Normalizing numerical data</li><li>K-Fold cross-validation to avoid overfitting [Hands-on]</li><li>Feature Engineering and the Curse of Dimensionality</li><li>Techniques for Imputation Missing Data</li><li>Oversampling, Undersampling, and SMOTE</li><li>Binning, Transforming, Encoding, Scaling and Shuffling</li><li>Dealing with Unbalanced data</li><li>Handling outliers [Hands-on]</li></ul><h3>2. Data Engineering in AWS</h3><ul><li>Introduction to Data Engineering</li><li>Amazon S3 [Hands-on]</li><li>Amazon S3 - Storage classes &amp; Lifecycle Rules Amazon S3 Security</li><li>Amazon S3 Security</li><li>Glue Data Catalog &amp; Crawlers [Hands-on]</li><li>Glue ETL [Hands-on]</li><li>Kinesis Data Streams &amp; Kinesis Data Firehose [Hands-on]</li><li>Kinesis Data Analytics [Hands-on]</li><li>Kinesis Video Streams</li><li>Kinesis ML Summary</li><li>Introduction Athena</li><li>AWS Data Stores in Machine Learning</li><li>AWS Data Pipelines</li><li>AWS Batch</li><li>AWS DMS - Database Migration Services</li><li>AWS Step Functions</li><li>Full Data Engineering Pipelines</li><li>AWS Containers</li><li>AWS Serverless</li></ul><h3> 3. Data Analysis in AWS</h3><ul><li>Introduction Data Analysis</li><li>Preparing Data for Machine Learning in a Jupyter Notebook.</li><li>Time Series-Trends and Seasonality</li><li>Amazon Athena [Hands-on]</li><li>Overview of Amazon Quicksight [Hands-on]</li><li>Types of Visualizations, and When to Use Them.</li><li>Hadoop Overview &amp; Elastic MapReduce (EMR) [Hands-on]</li><li>Apache Spark on EMR [Hands-on]</li><li>EMR Notebooks, Security, and Instance Types [Hands-on]</li></ul><h3>4. Modeling in AWS</h3><ul><li>Introduction to Modeling</li><li>Introduction to Deep Learning</li><li>Activation Functions</li><li>Convolutional Neural Networks</li><li>Recurrent Neural Networks</li><li>Deep Learning on EC2 and EMR</li><li>Tuning Neural Networks</li><li>Regularization Techniques for Neural Networks (Dropout, Early Stopping)</li><li>Grief with Gradients: The Vanishing Gradient problem</li><li>L1 and L2 Regularization</li><li>The Confusion Matrix</li><li>Precision, Recall, F1, AUC, and more</li><li>Ensemble Methods: Bagging and Boosting</li></ul><h3>5. Artificial Intelligence in AWS</h3><ul><li>Amazon Augmented AI</li><li>Amazon CodeGuru</li><li>Amazon Comprehend</li><li>Amazon Forecast</li><li>Amazon Fraud Detector</li><li>Amazon Kendra</li><li>Amazon Lex [Hands-on]</li><li>Amazon Personalize</li><li>Amazon Polly [Hands-on]</li><li>Amazon Rekognition [Hands-on]</li><li>Amazon Textract [Hands-on]</li><li>Amazon Transcribe</li><li>Amazon Translate [Hands-on]</li><li>AWS DeepComposer</li><li>AWS DeepLens</li><li>AWS DeepRacer</li><li>AWS Panorama</li><li>Amazon Monitron</li><li>Amazon HealthLake</li><li>Amazon Lookout for Vision</li><li>Amazon Lookout for Equipment</li><li>Amazon Lookout for Metrics</li></ul><h3>6. Machine Learning in SageMaker</h3>Introduction to SageMaker<ul><li>Understanding Machine Learning Pipeline</li><li>Why SageMaker ?</li><li>SageMaker for Machine Learning</li></ul>SageMaker Setup<ul><li>AWS S3 bucket creation</li><li>Notebook creation</li><li>Data collection, transformation &amp; upload to S3</li><li>Model Selection &amp; Training</li><li>Model Deployment</li><li>Model Validation</li></ul>SageMaker Built-in Algorithms<ul><li>BlazingText</li><li>DeepAR Forecasting</li><li>Factorization Machines</li><li>Image Classification Algorithm [Hands-on]</li><li>IP Insights</li><li>K-Means Algorithm</li><li>K-Nearest Neighbors (k-NN) Algorithm</li><li>Latent Dirichlet Allocation (LDA)</li><li>Linear learner algorithm [Hands-on]</li><li>Neural Topic Model (NTM) Algorithm</li><li>Object2Vec</li><li>Object Detection Algorithm</li><li>Principal Component Analysis (PCA) Algorithm</li><li>Random Cut Forest (RCF) Algorithm [Hands-on]</li><li>Semantic Segmentation</li><li>Sequence to Sequence (seq2seq)</li><li>XGBoost Algorithm</li></ul>Model Training &amp; Tuning<ul><li>Monitor &amp; Analyze Training jobs</li><li>Incremental Training</li><li>Hyperparameter Tuning</li></ul>Model Deployment<ul><li>Interface Pipeline</li><li>Train once &amp; Run Anywhere using Neo</li><li>Elastic Interface</li><li>Automatic Scaling</li><li>Standard Practices</li></ul>Using Machine Learning Frameworks with SageMaker<ul><li>Apache Spark with Amazon SageMaker</li><li>TensorFlow with Amazon SageMaker [Hands-on]</li><li>Apache MXNet with Amazon SageMaker</li><li>Scikit-learn with Amazon SageMaker</li><li>PyTorch with Amazon SageMaker</li></ul>Ground Truth using SageMaker<ul><li>Data Labelling</li><li>Input &amp; Output data to Ground Truth</li><li>Workforce for labelling - public, private, vendor</li></ul><h3>7. Monitoring &amp; Watching</h3>Monitoring &amp; Watching<ul><li>Monitoring with CloudWatch [Hands-on]</li><li>Logging with CloudWatch [Hands-on]</li><li>Logging in SageMaker API Calls with AWS CloudTrail [Hands-on]</li></ul>Using SageMaker SDK<ul><li>Understanding boto3</li><li>Actions - Common APIs</li><li>Understanding SageMaker Endpoint API</li></ul><h2>Program price includes:</h2><ul><li>32hrs of Training + 20hrs Hands-On</li><li>Service tax and VAT</li><li>Certification</li></ul><h2>Group Discount:</h2>Register as a group of 2 or more and get $20 off per person on registrations.<h2>Certification:</h2>Participants will be certified by Psitron Technologies Pvt. Ltd.<h2>Rules and Requirements:</h2><ul><li>Every participant must have one laptop of their own.</li><li>Required Software&#x27;s installation assistance will be provided from Psitron</li></ul><h2>For more information contact.</h2>Sarathkumar.CFounder &amp; CEOPsitron Technologies Pvt. Ltd.+918940876397 / +918778033930sarath@psitrontech.comwww.psitrontech.com<h2>P.S. - Today, don&#x27;t play it safe, play it smart.</h2>

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AWS Machine Learning Specialty Course

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