AI, Machine Learning & Deep Learning
Free
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).
The Course will be able to:
- 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
Who should attend:
- Software developers
- IT Consultants
- BigData Developers
- BigData Administrators
- Program Managers
- Anyone Passionate about ML
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 78
- Quizzes 0
- Duration 5 days
- Skill level All levels
- Language Machine Learning
- Students 50
- Certificate No
- Assessments Yes
You have 4 days 23 hours 59 minutes remaining for the course
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Day 1 : Module 1 - Demystify Machine Learning and Artificial Intelligence
- • Evolution of Machine Learning Copy
- • Define Machine Learning (ML) Copy
- • Define Supervised Learning Copy
- • Define Un-Supervised Learning Copy
- • Define reinforcement learning Copy
- • Define Semi-supervised Learning Copy
- • Define Federated Learning Copy
- • Understand concepts of AI, Deep Learning and NLP Copy
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Day 1 : Module 2 - Use Cases
- • Machine Learning in Banking and Finance Industry Copy
- • Machine Learning in Healthcare Copy
- • Machine Learning in Transportation Copy
- • Machine Learning in Government Copy
- • Machine Learning in Media and entertainment Copy
- • Top 10 AI predictions Copy
- • What next in AI? Copy
- • ML and AI industry insights Copy
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Day 2 : Module 1 - ML- Prerequisites Refreshers
- • Data Types (Numerical, categorical and Ordinal) Copy
- • Just enough Python for ML Copy
- • Lab : Simple python exercise Copy
- • Introduction to NumPy and simple lab on numpy Copy
- • Introduction to SciPy and simple lab on Scipy Copy
- • Introduction to Pandas and simple lab exercise Copy
- • Introduction to MatPlotLib and simple lab exercise Copy
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Day 2 : Module 2 - Hands on lab Sessions on Machine Learning and AI
- • Classification Lab – Classify images using Tensorflow and visualise using Matplotlib Copy
- • Clustering Lab – Customer segmentation Copy
- • Regression Lab – Predict pricing of house Scikit-learn NumPy and Pandas Copy
- • Recommendation Lab – Provide recommendations using Natural Language Processing using live data of training services company ( usingNltk tool kit) Copy
- • Sentiment Analysis Lab – Movie review ( Positive or negative) using Natural Language Processing Copy
- • Reinforcement Learning Lab – Place agent in one of the room and goal is to reach outside the building Copy
- • Association Lab – Perform Market basket analysis for e-commerce Copy
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Day 3 : AWS Cloud ML & AI Solutions (Pre-trained Models)
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Module 1 : Introduction to ML and AI tools from AWS
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• AWS Sagemaker – Overview and features
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• AWS Textract – overview and Features
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• AWS Translate – Overview and Features
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• AWS Transcribe – overview and features
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• AWS Rekognition – Overview and features I
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• Amazon Comprehend – NLP
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• AWS Polly – Overview and features
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• AWS Personalize – Overview and Features
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• Amazon DeepLens – Overview and Features
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• Amazon Forecast ( reinforcement learning) – Overview and Features
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• Amazon Lex – overview and features
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Day 4 : Azure Cloud ML & AI Solutions (Pre-trained Models)
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Module 1: Introduction to Azure Machine Learning
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• Azure machine learning overview.
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• Introduction to Azure machine learning studio.
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• Developing and hosting Azure machine learning applications
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• Hands-on lab sessions Lab
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Module 2 : Building Azure machine learning models with ML Studio
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Module 3 : Publish Predictive models as Azure Machine Learning services
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• Hands on Lab
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Module 4: Building Azure Machine Learning Models with Azure ML Services
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• Hands on Lab
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Day 5 : Google Cloud ML & AI Solutions (Pre-trained Models)
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Module 1 : Google Machine Learning AI Solutions Overview
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• Vision AI : Overview and Concepts
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• Video AI: Overview and Features
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• AI Platform Notebooks: Overview and Features
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• AI Platform Deep Learning VM Image :Overview and Features
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• Kubeflow: Overview and Features
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• Cloud TPU : Overview and Features
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• Natural Language : Overview and Features
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• Translation : Overview and Features
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• Cloud Speech-to-Text API : Overview and Features
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• Cloud Text-to-Speech API : Overview and Features
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• Dialogflow : Overview and Features
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• AutoMLTables : Overview and Features
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• Cloud Inference API : Overview and Features
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• Recommendations AI (beta) : Overview and Features
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• BigQueryML : Overview and Features
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• Cloud AutoML : Overview and Features
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Module 2 : Google cloud Machine Learning Labs
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• Lab1 : Implementing an AI Chatbot with Google Dialogflow
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• Lab 2 : Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API
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• Lab 3: Google Cloud
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• Lab 4 : User vision API
