Cloud Machine Learning Associate

  • This is a 5 day most powerful and unique course on Machine learning course covering 360 degree aspects of Machine Learning and Artificial Intelligence for intermediate level knowledge . It covers Machine Learning foundation for first 2 day program on understanding concepts on ML and AI with hands-on labs sessions . where you learn how to Train your own model with ML tools. The next 3 days you spend on AWS, Azure and Google cloud Machine learning and AI solutions for one day each respectively with concepts and hands-on labs ( to learn Trained models).

    • 5 days of instructor-led training
    • Machine learning foundation
    • Multi cloud ML and AI solutions ( AWS, Azure and Google Cloud)

    • Understand Machine Learning concepts
    • Differentiate between Supervised, Un-Supervised and Reinforcement learning
    • Understand Federated and semi-supervised learning
    • Understand Deep Learning and AI
    • Understand use cases of ML and AI in various industry
    • Quick hands-on labs with just enough python in 30 mins for ML and AI
    • Hands-on labs sessions on Pandas, Tensorflow, KNN, Scikit learn
    • Run programs on classification, clustering and reinforcement learning
    • Run programs for recommendations , Data visualization and sentiment Analysis
    • Deploy live chatbot using Google cloud solutions
    • Perform image, video, text, speech labs using Amazon cloud ML solutions
    • Perform Azure Ml studio labs and forecasting technique using Azure ML and AI solutions

  • This course is developed from a vendor neutral perspective from Cloud Enabled experts ,hence single vendor certification is not targeted here. You can appear vendor neutral certification exam from Cloud Enabled titled as “Cloud Enabled Certified Cloud Machine Learning Associate” . Exam mode dual factor. (A) a proctored exam is multiple choice and practical hands on labs (B) A small project to be successfully completed to achieve certification. The exam fee is 400 USD single attempt. We prepare you for jargon free and a real ML engineer role and skills you need rather than a vendor based approach.

    • Software developers
    • IT Consultants
    • BigData Developers
    • BigData Administrators
    • Program Managers
    • Anyone Passionate about ML

  • Day 1 & 2 : ML and AI Foundation ( Train Your own Models)
    Module 1 : Demystify Machine Learning and Artificial Intelligence

    • Evolution of Machine Learning
    • Define Machine Learning (ML)
    • Define Supervised Learning
    • Define Un-Supervised Learning
    • Define reinforcement learning
    • Define Semi-supervised Learning
    • Define Federated Learning
    • Understand concepts of AI, Deep Learning and NLP

    Module 2 : Use Cases

    • Machine Learning in Banking and Finance Industry
    • Machine Learning in Healthcare
    • Machine Learning in Transportation
    • Machine Learning in Government
    • Machine Learning in Media and entertainment
    • Top 10 AI predictions
    • What next in AI ?
    • ML and AI industry insights

    Module 3: ML- Prerequisites Refreshers

    • Data Types ( Numerical, categorical and Ordinal)
    • Just enough Python for ML
    • Lab : Simple python exercise
    • Introduction to NumPy and simple lab on numpy
    • Introduction to SciPy and simple lab on Scipy
    • Introduction to Pandas and simple lab exercise
    • Introduction to MatPlotLib and simple lab exercise

    Module 4: Hands on lab Sessions on Machine Learning and AI

    • Classification Lab – Classify images using Tensorflow and visualise using Matplotlib
    • Clustering Lab – Customer segmentation
    • Regression Lab – Predict pricing of house Scikit-learn NumPy and Pandas
    • Recommendation Lab – Provide recommendations using Natural Language Processing using live data of training services company ( using Nltk tool kit)
    • Sentiment Analysis Lab – Movie review ( Positive or negative) using Natural Language Processing
    • Reinforcement Learning Lab – Place agent in one of the room and goal is to reach outside the building
    • Association Lab – Perform Market basket analysis for e-commerce

    Day 3 : AWS Cloud ML & AI Solutions ( Pre-trained Models)
    Module 1 : Introduction to ML and AI tools from AWS

    • AWS Sagemaker – Overview and features
      • Labs : Deploy one click Jupyter notebooks(NB)
      • Labs : run sample Pandas programs on cloud jupyter NB
    • AWS Textract – overview and Features
      • Labs : Extract text from documents
    • AWS Translate – Overview and Features
      • Labs – translate content from English to Chinese language
    • AWS Transcribe – overview and features
      • Labs – convert speech to text
    • AWS Rekognition – Overview and features I
      • Labs – Object and scene detection
      • Labs – Image Moderation
      • Labs – Facial Analysis
      • Labs – Celebrity recognition
      • Labs – Face comparison
      • Labs – Text in Image
      • Labs – Video Analytics
    • Amazon Comprehend – NLP
      • Labs – Analyse unstructured text
    • AWS Polly – Overview and features
      • Labs – Text to Life like speech conversion
    • AWS Personalize – Overview and Features
    • Amazon DeepLens – Overview and Features
    • Amazon Forecast ( reinforcement learning) – Overview and Features
    • Amazon Lex – overview and features

    Day 4 : Azure Cloud ML & AI Solutions ( Pre-trained Models)
    Module 1: Introduction to Azure Machine Learning

    • Azure machine learning overview.
    • Introduction to Azure machine learning studio.
    • Developing and hosting Azure machine learning applications
    • Hands-on lab sessions Lab
      • Using Exercise and Calories dataset
      • Explore Azure Machine Learning Studio
      • Upload datasets, Create Experiments
      • How to import data from big data sources and define a data workflow in an experiment.

    Module 2 : Building Azure machine learning models with ML Studio

    • Prepare Azure SQL database, Import data, Visualize data
    • Train and evaluate a regression model and a classification model using exercise and calories data set.

    Module 3 : Publish Predictive models as Azure Machine Learning services

    • Significance of webservice
    • How to publish and test a webservice in ML Studio
    • Hands on Lab
      • Publish and test a webservice using ML Studio using exercise and calories dataset
      • Publishing and consuming a parameterized webservice

    Module 4: Building Azure Machine Learning Models with Azure ML Services

    • Introduction to Azure Machine Learning Services
    • How to build Azure machine learning models with ML services.
    • Hands on Lab
      • Building Azure machine learning models with ML services introduction
      • Electricity demand forecast

    Day 5 : Google Cloud ML & AI Solutions ( Pre-trained Models)
    Module 1 : Google Machine Learning AI Solutions Overview

    • Vision AI : Overview and Concepts
      • Analyze images in the cloud or at the edge
    • Video AI: Overview and Features
      • Precise video analysis — down to the frame
    • AI Platform Notebooks: Overview and Features
      • An enterprise notebook service to launch projects in minutes
    • AI Platform Deep Learning VM Image :Overview and Features
      • Preconfigured virtual machines for deep learning applications
    • Kubeflow: Overview and Features
      • The machine learning toolkit for Kubernetes
    • Cloud TPU : Overview and Features
      • Hardware designed for performance
    • Natural Language : Overview and Features
      • Multimedia and multi-language processing
    • Translation : Overview and Features
      • Fast, dynamic translation tailored to your content
    • Cloud Speech-to-Text API : Overview and Features
      • Speech recognition across 120 languages
    • Cloud Text-to-Speech API : Overview and Features
      • Lifelike text-to-speech interactions
    • Dialogflow : Overview and Features
      • Conversational experiences across devices and platform
    • AutoML Tables : Overview and Features
      • Build state-of-the-art ML models on structured data
    • Cloud Inference API : Overview and Features
      • Run large-scale correlations over typed time-series datasets
    • Recommendations AI (beta) : Overview and Features
      • Deliver highly personalized product recommendations at scale
    • BigQuery ML : Overview and Features
      • Build models with SQL
    • Cloud AutoML : Overview and Features
      • Train custom ML models quickly and easily

    Module 2 : Google cloud Machine Learning Labs

    • Lab1 : Implementing an AI Chatbot with Google Dialogflow

    The goal of this lab is to introduce the basics of Google Cloud Dialogflow by building a responsive chat bot, such as those handling support requests on websites. Demonstrates how to utilize this interactive AI in application development.

    • Lab 2 : Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API

    The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. In this lab you’ll send an image to the Cloud Vision API and have it identify objects, faces, and landmarks.

    • Lab 3: Google Cloud – Deploy Jupyter notebook instance with GPU and run sample pandas or classification example program
    • Lab 4 : User vision API to identify text from image sign board (OCR) which is in chinese language and translate the text to english using Google Translate api .

    • Gives an edge over other professionals in the same field, in term of pay package.
    • Customer are transitioning to AI enabled organization.
    • Helpful for People are trying to transition to data scientist roles from software engineer
    • The tool training helps to speak more confidently about this technology at my company when networking with others.

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