A .I

Course : Machine Learning, Deep Learning and Artificial Intelligence

Duration: 40 days

Module 1 – Python Programing

Objectives – At the end of this Module, you should be able to:

  • Introduction to Python Programming
  • Working with Numbers, Floats and Strings
  • Conditional Execution
  • Working with Functions and Recursions
  • Lists
  • List Comprehensions
  • More on Strings
  • Dictionaries
  • File IO
  • Regular Expressions
  • Classes and Objects
  • Debugging tools
  • Working with NumPy

Module 2 – Machine Learning

Objectives – At the end of this Module, you should be able to:

  • Introduction to Machine Learning
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees

Module 3 – Introduction to Deep Learning

Objectives – At the end of this Module, you should be able to:

  • Discuss the revolution of Artificial Intelligence.
  • Discuss the limitations of Machine Learning.

List the advantages of Deep Learning over Machine Learning.

  • Discuss Real-life use cases of Deep Learning.
  • Understand the Scenarios where Deep Learning is applicable.
  • Discuss relevant topics of Linear Algebra and Statistics.
  • Discuss Machine learning algorithms.
  • Discuss model parameters and optimization techniques.

Module 4 – Fundamentals of Neural Networks.

Objectives – At the end of this Module, you should be able to:

  • Define Neural Networks.
  • Discuss the Training Techniques of Neural Networks.
  • List Different Activation and Loss Functions.
  • Discuss the Different parameters of Neural Networks.
  • Gradient Descent.
  • Stochastic Gradient Descent.

Module 5: Fundamentals of Deep Networks

Objectives – At the end of this Module, you should be able to:

  • Define Deep Learning
  • Discuss the Architectural Principals of Deep Networks
  • List Different parameters of Deep Networks
  • Discuss the Building Blocks of Deep Networks

Module 6: Introduction to TensorFlow

Objectives – At the end of this Module, you should be able to:

  • Define TensorFlow.
  • Illustrate how TensorFlow works.
  • Discuss the Functionalities of TensorFlow.
  • Illustrate different ways to install TensorFlow.
  • Install TensorFlow.
  • Write and Run programs on TensorFlow.

Module 7: Convolutional Neural Networks (CNN)

Objectives – At the end of this Module, you should be able to:

  • Define CNNs.
  • Discuss the Applications of CNN.

 Explain the Architecture of a CNN.

  • List Convolution and Pooling Layers in CNN.
  • Understanding and Visualizing a CNN.
  • Discuss Fine-tuning and Transfer Learning of CNNs.

Module 8 – Recurrent Neural Networks (RNN)

Objectives – At the end of this Module, you should be able to:

  • Define RNN.
  • Discuss the Applications of RNN.
  • Illustrate how RNN is trained.
  • Discuss Long Short-Term memory(LSTM).

Module 9 – Artificial Intelligence

Objectives – At the end of this Module, you should be able to:

  • Introduction to Artificial Intelligence
  • AI Scenarios
  • Depth First Search
  • Breadth First Search
  • Dynamic Programming
  • A*Search

Module 10 – Project Work

Objectives – At the end of this Module, you should be able to:

  • Real Time use cases
  • Project Work