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Artificial Intelligence Foundation

Course Summary

Artificial Intelligence is the new science of the century. The characteristic features governing the human mind, being evident in machines, is an unprecedented possibility and has equally contributed to churning out new opportunities on a career front. 

The Certified Artificial Intelligence Expert course mainly focuses on employing the knowledge of artificial intelligence into the organizational functions. 


Course Objectives

The AI foundation course ensures that an individual is well equipped in the following areas:

  • Machine Learning Primer
  • Advanced Python for Deep Learning
  • Mathematics for Deep Learning 
  • Tensorflow for Deep Learning  
  • Deep Learning Foundation

Course Outline

Introduction to Artificial Intelligence

  • History of Artificial Intelligence (AI)
  • Five domains of AI
  • Why AI now?
  • Limitation of AI

Machine Learning Primer

  • Machine Learning Primer
  • Machine Learning core concepts, scalable algorithms, project workflow.
  • Objective Functions and Regularization
  • Understanding the Objective Function of ML Algorithms
  • Metrics, Evaluation Methods and Optimizers
  • Popular Metrics in Detail: R2 Score, RMSE, Cross-Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD, ADAM
  • Artificial Neural Network
  • ANN in detail, Forward Pass and Back Propagation
  • Machine Learning Vs Deep Learning
  • Core difference b/w ML and DL from an implementation perspective

Advanced Python For Deep Learning

  • Python Programming Primer
  •  Installing Python, Programming Basics, Native Data types
  •  Class, Inheritance and Magic Functions
  •  Python Classes, Inheritance Concepts, Magic Functions
  •  Special Functions in Python
  •  Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key Functions
  •  Decorators and Special Functions
  •  Decorators implementation with class
  •  Context Manager ‘with’ in Python
  •  Context Manager Application
  •  Exception Handling
  •  Try and Catch block
  • Python Package Management
  •  Bundling and export python packages

TensorFlow 2.0 and Keras for Deep Learning

  • TensorFlow 2.0 Basics
  •  TensorFlow core concepts, Tensors, core APIs
  •  Concrete Functions, Data Types, Control Statements
  •  Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
  •  Autograph eager execution
  •  tf.function autograph implementation
  •  Keras (TensorFlow 2.0 Built-in API) Overview
  •  Sequential Models, configuring layers, loading data, train and test, complex models, callbacks, save and restore Neural Network weights
  •  Building Neural Networks in Keras
  •  Building Neural networks from scratch in Keras

Mathematics for Deep Learning

  • Linear Algebra
  •  Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
  •  Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
  •  Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
  •  Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross-Entropy, Quadratic Loss Functions

Deep Learning Foundation

  • Deep Learning Network Concepts
  •  Core concepts of Deep Learning Networks
  •  Deep Dive into Activation Functions
  •  Building simple Deep Learning Network
  •  Tuning Deep Learning Network 

Who can benefit from the course?

  • Individuals who aspire to enter the field of AI
  • Professionals who work in the Machine Learning job roles

Prerequisites

  • Basic knowledge of AI and Machine Learning
  • Knowledge and skill on Python programming

Course Objectives

The AI foundation course ensures that an individual is well equipped in the following areas:

  • Machine Learning Primer
  • Advanced Python for Deep Learning
  • Mathematics for Deep Learning 
  • Tensorflow for Deep Learning  
  • Deep Learning Foundation

Course Outline

Introduction to Artificial Intelligence

  • History of Artificial Intelligence (AI)
  • Five domains of AI
  • Why AI now?
  • Limitation of AI

Machine Learning Primer

  • Machine Learning Primer
  • Machine Learning core concepts, scalable algorithms, project workflow.
  • Objective Functions and Regularization
  • Understanding the Objective Function of ML Algorithms
  • Metrics, Evaluation Methods and Optimizers
  • Popular Metrics in Detail: R2 Score, RMSE, Cross-Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD, ADAM
  • Artificial Neural Network
  • ANN in detail, Forward Pass and Back Propagation
  • Machine Learning Vs Deep Learning
  • Core difference b/w ML and DL from an implementation perspective

Advanced Python For Deep Learning

  • Python Programming Primer
  •  Installing Python, Programming Basics, Native Data types
  •  Class, Inheritance and Magic Functions
  •  Python Classes, Inheritance Concepts, Magic Functions
  •  Special Functions in Python
  •  Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key Functions
  •  Decorators and Special Functions
  •  Decorators implementation with class
  •  Context Manager ‘with’ in Python
  •  Context Manager Application
  •  Exception Handling
  •  Try and Catch block
  • Python Package Management
  •  Bundling and export python packages

TensorFlow 2.0 and Keras for Deep Learning

  • TensorFlow 2.0 Basics
  •  TensorFlow core concepts, Tensors, core APIs
  •  Concrete Functions, Data Types, Control Statements
  •  Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
  •  Autograph eager execution
  •  tf.function autograph implementation
  •  Keras (TensorFlow 2.0 Built-in API) Overview
  •  Sequential Models, configuring layers, loading data, train and test, complex models, callbacks, save and restore Neural Network weights
  •  Building Neural Networks in Keras
  •  Building Neural networks from scratch in Keras

Mathematics for Deep Learning

  • Linear Algebra
  •  Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
  •  Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
  •  Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
  •  Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross-Entropy, Quadratic Loss Functions

Deep Learning Foundation

  • Deep Learning Network Concepts
  •  Core concepts of Deep Learning Networks
  •  Deep Dive into Activation Functions
  •  Building simple Deep Learning Network
  •  Tuning Deep Learning Network 

Who can benefit from the course?

  • Individuals who aspire to enter the field of AI
  • Professionals who work in the Machine Learning job roles

Prerequisites

  • Basic knowledge of AI and Machine Learning
  • Knowledge and skill on Python programming

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