Cloud Machine Learning Associate Training
Training Calendar for Machine learning
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IT Includes
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 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 featuresModule 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
Categorizing your data
Importing data to Azure machine learning,
Exploring and transforming data in Azure machine learning
Hands on labs
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
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.
Lab:
Building Azure machine learning models with ML services introduction
Electricity demand forecastModule 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.
Training Delivery Modes
Mindbel learning offers 4 learning modes to choose
Classroom Training
Interactive sessions by expert and accredited trainers in classroom
Online Training
Industry experienced trainers led online sessions
Corporate group training for corporates
Flexible delivery methods are available depending on your learning style.
Focused One to One training
You will learn from qualified, accredited, certified and highly experienced trainers in classroom
Our Blogs
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