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Artificial Intelligence Expert

Course Summary

Artificial Intelligence is an opportunity. Every organization is in an effort to leverage the possibilities. The Certified Artificial Intelligence Expert talks about the incorporation of data science into human resource activity.


Course Objectives

Given the successful completion of the course, you will be equipped with the following:

  • Artificial Intelligence -an Overview 
  • Statistics
  • Incorporation of Artificial Intelligence and Machine Learning into the business
  • Helps to lead forward in the Data Science career trajectory  

Course Outline

Artificial Intelligence Foundation

  • Introduction to Artificial Intelligence (AI)
  • AI Data Strategy
  • AI Ethics, Issues and Concerns
  • AI Challenges
  • Use Cases, and Adaption

Machine Learning Foundation

  • 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 Cnnswith 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 RL
  • Model-Based Method
  • Dynamic Programming Model Free Methods

Deep Reinforcement Learning

  • Architectures Of Deep Q Learning
  • Deep Q Learning
  • Policy Gradient Methods
  • Actor-Critic 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

  • Amazon Web Services (AWS)
  • Deploying Deep Learning Models Using AWS Sagemaker

Target Audience

  • Individuals aspiring to make a career in Artificial Intelligence
  • Professionals working in the operations domain 

Prerequisite

  • No mandatory prerequisite
  • Experience in the AI domain recommended

Course Objectives

Given the successful completion of the course, you will be equipped with the following:

  • Artificial Intelligence -an Overview 
  • Statistics
  • Incorporation of Artificial Intelligence and Machine Learning into the business
  • Helps to lead forward in the Data Science career trajectory  

Course Outline

Artificial Intelligence Foundation

  • Introduction to Artificial Intelligence (AI)
  • AI Data Strategy
  • AI Ethics, Issues and Concerns
  • AI Challenges
  • Use Cases, and Adaption

Machine Learning Foundation

  • 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 Cnnswith 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 RL
  • Model-Based Method
  • Dynamic Programming Model Free Methods

Deep Reinforcement Learning

  • Architectures Of Deep Q Learning
  • Deep Q Learning
  • Policy Gradient Methods
  • Actor-Critic 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

  • Amazon Web Services (AWS)
  • Deploying Deep Learning Models Using AWS Sagemaker

Target Audience

  • Individuals aspiring to make a career in Artificial Intelligence
  • Professionals working in the operations domain 

Prerequisite

  • No mandatory prerequisite
  • Experience in the AI domain recommended

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