Certification Program in Artificial Intelligence and Deep Learning

[Parveen Komal]

Introduction to Artificial Intelligence and Deep Learning

• Overview and Applications of AI
• Building AI Solutions using Deep Learning
• Deep Learning – Neural Network using Python & R
• Introduction to Python Libraries – Tensorflow, Keras, OpenCV
• Perception Algorithm and Back propagation Algorithm
• Artificial Neural Network(ANN) or Multilayer Perceptron

Data Mining Supervised Learning/Machine Learning

• Convolution Neural Network(CNN)
• Image Processing and Computer Vision
• Recurrent Neural Network(RNN)
• Long Short-term Memory(LSTM)

Data Mining Unsupervised Learning

• Gated Recurrent Network (GRU)
• Auto Encoder, Restricted Boltzmann Machine (RBM)
• Deep Belief Networks (DBN)
• Generative Adversarial Networks (GANs)
• Reinforcement Learning and Q-Learning

Key benefits/Learning Outcomes

• Understand about the Gated Recurrent Networks (GRU)
• Learn on how to build AI systems using Deep Learning Algorithms
• Be able to deal with unstructured data such as images, videos, text etc.
• Be able to implement Deep Learning Solutions and image processing applications using convolutional networks
• Be introduced to analyze sequence data and perform Text analytics and Natural Language Processing (NLP) using recurrent networks
• Be able to effectively use various Python libraries such as Keras, Tensorflow, OpenCV, etc. which are used in solving AI and Deep Learning problems

Who should attend this program?

All those participants who have:

• Basic idea about programming
• Basic mathematical knowledge should be good to start with the program
• Candidates aspiring to become Data Scientists or Deep Learning & AI experts
• Analytics Managers / Professionals, Business Analyst, Developer
• People who are looking to build a career in Machine Learning, Deep Learning and AI
• Employees of organizations, who are planning to focus on building AI applications
• Students can also take up this program to guide their career towards the space of AI

System hardware and Software Requirements to attend this session:
A system with a min of 4 GB RAM with decent processor (i5 core) should do.
The below softwares are required.

• Python and Anaconda (IDE)
• R and RStudio
• MS Office

[Vicky Rajput]

• Basic idea about programming
• Basic mathematical knowledge should be good to start with the program
• Candidates aspiring to become Data Scientists or Deep Learning & AI experts
• Analytics Managers / Professionals, Business Analyst, Developer
• People who are looking to build a career in Machine Learning, Deep Learning and AI
• Employees of organizations, who are planning to focus on building AI applications

[Rohan Tiwadi]

• Overview and Applications of AI
• Building AI Solutions using Deep Learning
• Deep Learning – Neural Network using Python & R
• Introduction to Python Libraries – Tensorflow, Keras, OpenCV
• Perception Algorithm and Back propagation Algorithm
• Artificial Neural Network(ANN) or Multilayer Perceptron

[Vicky Rajput]

• Be able to implement Deep Learning Solutions and image processing applications using convolutional networks
• Be introduced to analyze sequence data and perform Text analytics and Natural Language Processing (NLP) using recurrent networks
• Be able to effectively use various Python libraries such as Keras, Tensorflow, OpenCV, etc. which are used in solving AI and Deep Learning problems

[Vicky Rajput]

• Convolution Neural Network(CNN)
• Image Processing and Computer Vision
• Recurrent Neural Network(RNN)
• Long Short-term Memory(LSTM)

[Rohan Tiwadi]

• Be able to implement Deep Learning Solutions and image processing applications using convolutional networks
• Be introduced to analyze sequence data and perform Text analytics and Natural Language Processing (NLP) using recurrent networks
• Be able to effectively use various Python libraries such as Keras, Tensorflow, OpenCV, etc. which are used in solving AI and Deep Learning problems