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Supervised Machine Learning Regression and Classification

Supervised Machine Learning Regression and Classification

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Overview

Course Outline

Schedule & Fees

Overview
Why Attend

AI offers businesses valuable insights into customer behavior trends and operational patterns, helping them develop new products. Today, many leading companies leverage AI fields like machine learning to enhance their operations. By the end of this specialization, participants will have mastered key concepts and acquired practical skills to effectively apply machine learning to tackle complex real-world challenges. For those aiming to enter the AI field or build a career in machine learning, this new Machine Learning Specialization is the perfect starting point.

Course Objectives

By the end of the course, participants will be able to:

Who should attend? 

  • Software Engineers
  • Enthusiasts about AI

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

  • have a broad understanding of machine learning, its concepts, and its methods.
  • Implementing fundamental machine learning algorithms such as back propagation and k-means clustering.
  • Tackling tasks such as multi-class classification and anomaly detection.
  • The ability to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.
  • Get real-world experience on how to implement AI in the field of your work.
Course Outline
  • Introduction to Machine Learning
    • Applications of machine learning
    • What is machine learning?
    • Supervised learning
    • Unsupervised learning
    • Jupyter Notebooks
    • Linear regression model
    • Cost function formula
    • Cost function intuition
    • Visualizing the cost function
    • Visualization examples
    • Gradient descent
    • Implementing gradient descent
    • Gradient descent intuition
    • Learning rate
    • Gradient descent for linear regression
    • Running gradient descent
  • Regression with multiple input variables
    • Multiple features
    • Vectorization
    • Gradient descent for multiple linear regression
    • Feature scaling
    • Checking gradient descent for convergence
    • Choosing the learning rate
    • Feature engineering
    • Polynomial regression
  • Classification
    • Logistic regression
    • Decision boundary1
    • Cost function for logistic regression
    • Simplified Cost Function for Logistic Regression
    • Gradient Descent Implementation
    • The problem of overfitting
    • Addressing overfitting
    • Cost function with regularization
    • Regularized linear regression
    • Regularized logistic regression
Schedule & Fees
2026 Schedule & Fees
USD 3,500
Per participant
  • 1 - 5 Nov, 2026
  • English
USD 3,500
Per participant
  • 1 - 5 Nov, 2026
  • Arabic
USD 3,500
Per participant
  • 15 - 19 Nov, 2026
  • English
USD 3,500
Per participant
  • 15 - 19 Nov, 2026
  • Arabic
2027 Schedule & Fees
USD 3,500
Per participant
  • 31 Jan - 4 Feb, 2027
  • English
USD 3,500
Per participant
  • 31 Jan - 4 Feb, 2027
  • Arabic
USD 3,500
Per participant
  • 14 - 18 Feb, 2027
  • English
USD 3,500
Per participant
  • 14 - 18 Feb, 2027
  • Arabic
USD 3,500
Per participant
  • 2 - 6 May, 2027
  • English
USD 3,500
Per participant
  • 2 - 6 May, 2027
  • Arabic
USD 3,500
Per participant
  • 16 - 20 May, 2027
  • English
USD 3,500
Per participant
  • 16 - 20 May, 2027
  • Arabic
USD 3,500
Per participant
  • 1 - 5 Aug, 2027
  • English
USD 3,500
Per participant
  • 1 - 5 Aug, 2027
  • Arabic
USD 3,500
Per participant
  • 15 - 19 Aug, 2027
  • English
USD 3,500
Per participant
  • 15 - 19 Aug, 2027
  • Arabic
USD 3,500
Per participant
  • 31 Oct - 4 Nov, 2027
  • English
USD 3,500
Per participant
  • 31 Oct - 4 Nov, 2027
  • Arabic
USD 3,500
Per participant
  • 14 - 18 Nov, 2027
  • English
USD 3,500
Per participant
  • 14 - 18 Nov, 2027
  • Arabic