AI/ML Engineer – Stage 2 provides in-depth knowledge about supervised learning algorithms, also known as supervised machine learning, which is a subcategory of machine learning and artificial intelligence. It is defined by its use of labelled datasets to train algorithms to classify data or predict outcomes accurately.

The course is designed with the delivery of adequate theoretical and practical knowledge required to develop your own solutions for real-world problems with supervised learning algorithms.

The course is further enhanced with a practical and hands-on approach to utilize the innovative tools and technologies you learned during stage 1 to apply the advanced theoretical knowledge in supervised learning approaches. In addition, the Centre for Open and Distance Education (CODE) at SLIIT promotes and delivers the environment for the participants to achieve a global certification in the AI discipline with a systematic approach to coursework.

Course content will be available for students at course enrollment and students can complete the lessons in a self-paced manner. To complete a lesson, you need to undertake a quiz and score more than 50%.

Course curriculum

    1. Supervised Learning

    2. Simple Linear Regression

    3. Multiple Linear Regression

    4. Linear Regression using Python

    5. Quiz 1 Materials

    6. Quiz 1

    1. Polynomial Regression

    2. Choosing the Right Model

    3. Polynomial Regression using Python

    4. Quiz 2 Materials

    5. Quiz 2

    1. Logistic Regression 1

    2. Logistic Regression 2

    3. Quiz 3

    1. Decision Tree & Random Forest 1

    2. Decision Tree & Random Forest 2

    3. Decision Tree & Random Forest 3

    4. Decision Tree & Random Forest 4

    5. Quiz 4

    1. Naive bayes/Support Vector Machine 1

    2. Naive bayes/Support Vector Machine 2

    3. Naive bayes/Support Vector Machine 3

    4. Quiz 5

    1. Suggest the most suitable algorithm for the following scenarios.

About this course

  • Free
  • 24 lessons