Artificial Intelligence with Python & TensorFlow 2.0

Artificial Intelligence with Python & TensorFlow 2.0

Deep Learning is a sub field of Machine Learning and a branch of broader Artificial Intelligence. In Deep Learning, machines learn to perform tasks using what is called Artificial Neural Networks. These Neural Networks are inspired by the biological neural networks that constitute the brain of living organisms. In today’s world it is being used in classifying images, detecting objects in video and images, facial recognition, speech recognition, analyze sentiment in texts, detect tumors in medical images, self-driving cars, self-flying autonomous drones and many more.

In this course we will start from very beginning to the point where you can actually build and launch several of these examples in real world. This course will give you practical knowledge on how to get started with deep learning and will help you explore state-of-the-art models, use them in your own project and convert your regular project into projects that are powered by an AI.

Why should you join:

  1. Learn Concepts & Implementation of Advanced Computer vision based Deep Learning models
  2. Learn Concepts & Implementation of Advanced Natural Language Processing based Deep Learning models
  3. Learn Concepts & implementation of State-of-the-Art Deep Learning Architectures
  4. Learn Concepts & Implementation of Generative Adversarial Networks (GANs) and Generative Deep Learning Models
  5. Learn Deploying Deep Learning Models on cloud, mobile devices and embedded devices
  6. Learn to Create your own Datasets for Deep Learning models
  7. Learn by doing. Each module consists of a project work to provide hands-on experience
  8. Basics of Machine Learning & Deep Learning covered for totally newcomers
  9. Python intro module provided for non-python programmers
  10. Every task is taught and implemented using latest TensorFlow 2.0 framework

What you’ll learn

  • Python & the working environment
  • Machine Learning Basics
  • Intro to TensorFlow & TF.Keras
  • Deep Learning Basics
  • Intro to Computer Vision
  • Intro to Natural Language Processing
  • Deep Learning + Computer Vision Usage
  • Deep Learning + NLP usage
  • Deep Dive into Conv Nets and emerging Architectures
  • Applications of Computer Vision
  • Deep Dive into RNNs, LSTMS & Attention based Models
  • Exploring Datasets for Deep Learning
  • Implementing State of The Art Models
  • Deploying Deep Learning Models
  • Generative Deep Learning


Career Opportunities

  • AI Engineer
  • ML Engineer
  • NLP Specialist


Student looking to join this course should have minimum skill set equivalent to Fundamentals of programming

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