Book Online Tickets for Training for IT Professionals: Practical, Banaglore. The proposed training is for working professionals who need to understand the nuts and bolts of building AI/Deep learning applications from a practical perspective. This will be done in a hands-on mode combined with an in-depth conceptual rendering o

Training for IT Professionals: Practical AI

 

  • AiProf

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    INR 49999
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  • Earlybird

    Sale Date Ended

    INR 47499
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About The Event

The proposed training is for working professionals who need to understand the nuts and bolts of building AI/Deep learning applications from a practical perspective. This will be done in a hands-on mode combined with an in-depth conceptual rendering of relevant concepts. You will eventually deploy your Tensorflow based deep learning models on industry-leading platforms such as AmazonSageMaker, Google Cloud Platform (GCP) ML Engine and Azure AI.

While several variants of AI training exist in the market, this training is intended to represent a complete practical hands-on oriented approach to equip developers with the know-how to build AI applications for your company – right from modeling to production deployment.

 

Contents

Week One
  • Introduction to AI
    • What is AI
    • Use cases
    • Tech Stack
    • AI vs ML vs DL
  • Setting up the Development Environment
    • Anaconda
    • Jupyter notebooks
    • Refresher on Python
    • Intro to numpy and Pandas
  • In class coding assignment
Week Two
  •  Basics of ML
    • Unsupervised, Supervised and Reinforcement
  • Unsupervised ML at a glance
    • Clustering, Recommendation Systems
    • Code walkthroughs using SkLearn
  • Supervised ML at a glance
    • Classification ( Logistic Regression, Decision Trees)
    • Linear Regression 
    • Code walkthroughs using SKLearn
  • In class coding assignment
Week 3
  • Basics of Deep Learning
    • Introduction to Neural networks
    • Coding a simple neural network in Keras/Tensorflow
  • Deep dive on Training, Loss functions, gradient descent, and back Propagation
  • Strategies to handle Overfitting and Underfitting
  • In class coding assignment( Auto-encoders
Week Four
  • Computer vision and Deep Learning
    • Introduction to image processing
    • Use cases
  • Convolutional Neural Networks ( CNN )
    • Introduction
    • Using OpenCV framework
    • Image processing using CNN
  • In class coding assignment on CNN
  • Natural Language Processing (NLP) and Deep Learning
    • Introduction
    • Use cases
  • Recurrent Neural Networks ( RNN )
    • Introduction
    • Walkthrough of an NLP use case using RNN
  • In class coding assignment on RNN (Sentiment Analysis)
Week Five
  • Practical Considerations of Machine Learning
    • Overfitting vs underfitting
    • Weight Initialization
    • Early stopping
    • Hyperparameter tuning
    • Normalization
    • Dropouts
    • Dataset design and understanding biases in data
    • Training on GPUs vs CPUs
  • Conversational AI
    • Introduction
    • Chatbot code example
  • In class coding assignment – write your own chatbot
  • Emerging areas
    • Attention networks
    • Generative Adversarial Networks ( GANs )
  • Introduction to Amazon SageMaker
    • Setting up Amazon Account
    • Development to Deployment workflow
    • Walkthrough of a sample model deployment
  • In class coding project
Remote Monitoring Take    Home    Capstone    -    Review    Offline    Over    Email    -    Four    weeks    after    training

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