Supervised Machine Learning Regression and Classification |Plus

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

4–8 Oct 2026

Language English
City

USD 3,000

Per participant

4–8 Oct 2026

Language Arabic
City

USD 3,000

Per participant

18–22 Oct 2026

Language English
City

USD 3,000

Per participant

18–22 Oct 2026

Language Arabic
City

USD 3,000

Per participant

2027 Schedule & Fees

3–7 Jan 2027

Language English
City

USD 3,000

Per participant

3–7 Jan 2027

Language Arabic
City

USD 3,000

Per participant

17–21 Jan 2027

Language English
City

USD 3,000

Per participant

17–21 Jan 2027

Language Arabic
City

USD 3,000

Per participant

4–8 Apr 2027

Language English
City

USD 3,000

Per participant

4–8 Apr 2027

Language Arabic
City

USD 3,000

Per participant

18–22 Apr 2027

Language English
City

USD 3,000

Per participant

18–22 Apr 2027

Language Arabic
City

USD 3,000

Per participant

4–8 Jul 2027

Language English
City

USD 3,000

Per participant

4–8 Jul 2027

Language Arabic
City

USD 3,000

Per participant

18–22 Jul 2027

Language English
City

USD 3,000

Per participant

18–22 Jul 2027

Language Arabic
City

USD 3,000

Per participant

3–7 Oct 2027

Language English
City

USD 3,000

Per participant

3–7 Oct 2027

Language Arabic
City

USD 3,000

Per participant

17–21 Oct 2027

Language English
City

USD 3,000

Per participant

17–21 Oct 2027

Language Arabic
City

USD 3,000

Per participant