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

 

  • Earlybird

    Last Date: 15-03-2019

    INR 47499
  • AiProf

    Start Date: 15-03-2019

    INR 49999
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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

a. Brief Introduction to AI
       - What is AI
       - Use cases
       - Tech Stack
       - AI vs ML vs DL

b. Setting up the Development Environment
       - Anaconda
       - Jupyter notebooks
       - Refresher on Python
       - Intro to numpy and Pandas
c. In class coding assignment

Week Two a. Basics of ML
- Unsupervised , Supervised and Reinforcement
b. Unsupervised ML at a glance
- Clustering , Recommendation Systems
- Code walkthroughs using SkLearn
c. Supervised ML at a glance
- Classification ( Logistic Regression , Decision Trees)
- Linear Regression
- Code walkthroughs using SKLearn
d. In class coding assignment
Week Three a. Basics of Deep Learning
- Introduction to Neural networks
- Coding a simple neural network in Keras/Tensorflow
b. Deep dive on Training, Loss functions, gradient descent and back Propagation
c. Strategies to handle Overfitting and Underfitting
d. In class coding assignment( Auto-encoders)
Week Four a. Computer vision and Deep Learning
- Introduction to image processing
- Use cases
b. Convolutional Neural Networks ( CNN )
- Introduction
- Using OpenCV framework
- Walkthrough of an image processing example using CNN
c. In class coding assignment on CNN
d. Natural Language Processing (NLP) and Deep Learning
- Introduction
- Use cases
e. Recurrent Neural Networks ( RNN )
- Introduction
- Walkthrough of an NLP use case using RNN
f. 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
b. Conversational AI
- Introduction
- Walkthrough on chatbot code example
c. In class coding assignment – write your own chatbot
d. Emerging areas
- Attention networks
- Generative Adversarial Networks ( GANs )
e. Introduction to Amazon SageMaker
- Setting up Amazon Account
- Development to Deployment workflow
- Walkthrough of a sample model deployment
f. In class coding project
Remote Monitoring Take    Home    Capstone    -    Review    Offline    Over    Email    -    Four    weeks    after    training

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