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?
SoftwareEngineers
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
There are no public sessions remaining for the course this year. Please contact us if you wish to have a customized in-house session, or to find out how an additional public session can be scheduled.