Artificial Intelligence has been successful in bringing revolution across industries. It has become ubiquitous and is greatly contributing to change. The idea of incorporating human intelligence into machines has ushered in curiosity for further experimentation. Today AI is recognized as a branch of computer science and is being widely used in areas like Business, Medicine, Education, Customer Service, etc. Apart from technology, AI is now identified as a way out, for dealing with complexities.
Certified Artificial Intelligence Engineer Course Module
Artificial Intelligence Foundation
- Introduction To Artificial Intelligence (AI)
- AI Data Strategy
- AI Ethics
- Issues And Concerns
- AI Challenges
- Use Cases And Adoption
Machine Learning
- Data Science And ML Introduction
- Mathematics For ML
- Learning Methods
- Popular ML Algorithms
- Building Classification Models
- Building Regression Models
- Beyond Machine Learning
Tensorflow 2.X Platform
- Tensorflow Introduction
- Tensorflow Basic Concepts
- Installation And Basic Operations In Tf 2.X,Tf 2.0 Eager
- Mode, Tensorflow 2.X – Keras
Core Learning Algorithms
- Core Learning Algorithms Introduction
- Regression With Tensorflow
- Classification With Tensorflow
Neural Networks
- Structure Of Neural Networks,
- Neural Network – Core Concepts
- Feed Forward Algorithm
- Backpropagation
- Building Neural Network From Scratch Using Numpy
Implementing Deep Neural Networks
- Introduction To Neural Networks With Tf2.X
- Simple Deep Learning Model In Keras (Tf2.X)
- Building Neural Network Model In Tf 2.0 For Mnist Dataset
Deep Computer Vision – Convolutional Neural Networks
- Convolutional Neural Networks (Cnns) Introduction
- Cnns With Keras
- Transfer Learning In Cnn
- Style Transfer, Flowers Dataset With Tf2.X
- Examining X-Ray With Cnn Model
Recurrent Neural Network
- Rnn Introduction
- Sequences With Rnns
- Long Short-Term Memory Networks (Lstm Rnns) And Gru
- Examples Of Rnn Applications
Natural Language Processing
- Natural Language Processing Introduction,
- NLP With Rnns
- Creating Model
- Transformers And Bert
- State Of Art Nlp And Projects
Reinforcement Learning
- Markov Decision Process
- Fundamental Equations In R,
- Model-Based Method
- Dynamic Programming Model Free Methods
Deep Reinforcement Learning
- Architectures Of Deep Q Learning
- Deep Q Learning
- Policy Gradient Methods
Generative Adversarial Network (Gan)
- Gan Introduction
- Core Concepts Of Gan
- Building Gan Model With Tensorflow 2.X
- Gan Applications
Deploying Deep Learning Models In The Cloud (Aws)
- Amazon Web Services (Aws)
- Deploying Deep Learning Models Using Aws Sagemaker
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