Artificial Intelligence (AI) Course Training

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Batch Starts
on   Dec 23 2019
3 months, 9 hours per week.
Course Fee
25,000 to 40,000

About EduAstrum

We at EduAstrum realize the value of Lifelong learning. EduAstrum is devoted to excellence in teaching, learning and to develop leaders in multiple disciplines who make a difference globally. The culture of EduAstrum is diverse and comprehensive; we strive to build personal and professional skills. There is no lack of people with talent in the world, but not every talented person gets the opportunity to utilize and showcase their talent. No matter where you are in the world, no matter what your background, we will give you a springboard to launch yourselves from. We provide learning resources and teachers to enable you to acquire skills and to enrich your lives at any time.

What Skills Will You Learn?

In this Artificial Intelligence (AI) course, you will be able to

  • Understand the basics of AI and how these technologies are re-defining the AI industry
  • Learn the key terminology used in AI space
  • Learn major applications of AI thru use cases

Things You Will Learn

Module 1

Introduction To AI And DL

Understand what is Artificial Intelligence and Deep Learning. Understand about the various job opportunities for AI and DL. You will learn about the most important Python libraries for building AI applications. These MUST KNOW Python libraries for Deep Learning are being upgraded on an extremely rapid pace and keeping abreast of the changes is pivotal for the success of AI experts.

Module 2

Introduction To Mathematical Foundations

Understand about the various mathematical concepts which are important to learn AI implementations. These concepts will help to understand Deep Learning concepts in detail. It will also serve as a refresher for learning various Neural Network algorithms, which are synonymous to Deep Learning.

Module 3

Introduction To The Perceptron Algorithm, ANN / MLP

Understand about the basics of the first algorithm – Perceptron Algorithm, its drawbacks and how we can overcome those challenges using Artificial Neural Network or Multilayer Perceptron Algorithm. The various activation functions will be understood in detail and practical exposure to R programming and Python programming is the highlight of this module.

Module 4

Various Hyper Parameters In Neural Networks

Learn about the various Error functions, which are also called Cost functions or Loss functions. Also, understand about the entropy and its use in measuring error. Understand the various optimization techniques, drawbacks and ways to overcome the same. This you will learn alongside various terms in implementing neural networks.

Module 5

Various Outcomes-Based MLP And Regularization Techniques

Learn about practical applications of MLP when output variable is continuous and discrete in two categories and multi-category. Understand also about handling balanced vs imbalanced datasets. Learn about techniques to avoid overfitting and various weight initialization techniques.

Module 6

Image Processing / Computer Vision Using OpenCV

Though CNN has replaced most of the computer vision and image processing concepts, a few application require the knowledge of Computer vision. We will learn about the application using the defacto library OpenCV for image processing. How to build machine learning models when we have limited data is explained as part of this module.

Module 7

Convolution Neural Network (CNN) And Variants

Understand about the various layers of CNN and understand how to build the CNN model from scratch as well as how to leverage upon the CNN model which is pre-trained. Understand about the best practices in building CNN algorithm and variants of convolution neural network.

Module 8

Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) And Gated Recurrent Unit (GRU)

Understand about how to deal with sequence data including textual data as well as time series data and audio processing. Understand about advanced RNN variant models including LSTM algorithm and GRU algorithm. Also learn about bidirectional RNN, LSTM and deep bidirectional RNN and LSTM. Learn about various unstructured textual pre-processing techniques.

Module 9

Generative Deep Learning, Generative Adversarial Networks (GANs), Autoencoders

Understand about unsupervised learning algorithm such as GAN as well as Autoencoders. GANs are used extensively in artificially generating speech, images which can be used in computer games. Deep Dream is such an algorithm which using GAN to generate images. Autoencoders will take input as an image and traverse through the network and then regenerates the same image. Learn about how these intermediate layer representations can be used in other neural network deep learning models.

Module 10

AI Game Playing, Reinforcement Learning, Advanced Techniques

Reinforcement learning is majorly used in AI-based games. Q-learning is one such Reinforcement machine learning algorithm which is using in game theory. Finally, any of the ongoing Kaggle competition will be the prime focus and to be in the top 100 will be of prime importance. This will bring optimal visibility of the profiles to the employers and participants can be directly hired.

Course Modules

Artificial Intelligence (AI) is the next big thing in the technology field and a large number of organizations are already implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course will provide you with a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.

Who Should Take This Course?

EduAstrum’s course on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence. This course doesn’t need any programming skills and is best suited for

  • Management and Non-technical participants
  • Students who want to learn Artificial Intelligence
  • Newbies who are not familiar with AI or its implication

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