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

Name: Training for IT Professionals: Practical AI
Start: 2019-05-04T09:00:00+05:30
End: 2019-06-08T18:00:00+05:30
Location: HSR Layout, Banaglore: Banaglore

Saturday, 4th May 2019 - Saturday, 8th Jun 2019 | 09:00 AM to 06:00 PM IST

HSR Layout, Banaglore, Banaglore, Karnataka, India

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INR 47499

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Event ID : 193445

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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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