Machine Learning - Batangas State University, The National Engineering University

19 HOURS

ONLINE

ENGLISH

19 HOURS

ONLINE

ENGLISH

In this Machine Learning course, we will equip you with a strong foundation in the critical concepts and practical skills needed to work with classical machine learning models. The journey begins with understanding the core principles, supported by a healthy balance of theoretical knowledge and hands-on exercises to grasp various machine learning techniques and their practical applications. You’ll become adept at preprocessing and analyzing data, building resilient models, and evaluating their performance to make informed, data-driven decisions. Suitable for both beginners and those looking to build on their existing knowledge, this course aims to empower you with the competencies required to tackle real-world challenges effectively using classical machine learning models.

What You'll Learn

At the end of this course, learners will be able to demonstrate a comprehensive understanding of the fundamental concepts, principles, and techniques of machine learning. They will also be equipped to apply their knowledge of machine learning to real-world scenarios. Furthermore, they will develop the ability to critically analyze and evaluate machine learning models, synthesize their knowledge and skills to construct and refine these models, and evaluate the effectiveness and performance of machine learning models.

Skills You'll Gain

Microsoft Azure
Machine learning

Roles

Data Scientist
AI Engineer

What You'll Learn

Skills You'll Gain

Microsoft Azure
Machine learning

Role

Data Scientist
AI Engineer

Course Outline

Module 1: Introduction to Data for Machine Learning
Module 2: Explore and Analyze Data with Python
Module 3: Train and Understand Regression Models in Machine Learning
Module 4: Refine and Test Machine Learning Models
Module 5: Train and Evaluate Regression Models
Module 6: Create and Understand Classification Models in Machine Learning
Module 7: Customize Architectures and Hyperparameters Using Random Forest
Module 8: Confusion Matrix and Data Imbalances
Module 9: Optimize Model Performance with ROC and AUC
Module 10: Train and Evaluate Classification Models
Module 11: Train and Evaluate Clustering Models
Module 12: Train and Evaluate Deep Learning Models

Required Resources

Laptop, Intel Core i5 or higher, 16GB, 1TB Storage, Graphics Card (Hardware); Microsoft Azure (Software); Adequate Internet Connection (Network)

Pre-Requisites

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Assessment

In this Machine Learning course:

One (1) diagnostic assessment is available, conducted synchronously, and is knowledge-based, with the flexibility for learners to choose between remote or on-site participation.
Two (2) formative assessments are offered, one focused on knowledge and the other on performance. Both are available asynchronously, allowing learners to complete them at their own pace, and learners have the option to participate remotely or on-site.
A performance-based summative assessment is to be conducted synchronously, providing learners with the choice of remote or on-site participation.

Credit and Recognition

Upon successful completion of the Machine Learning course, learners will receive a Certificate of Completion. This certificate serves both as a recognition of acquired expertise in machine learning and as a foundational step toward more advanced studies in the field of Data Science.

This course is facilitated by a Microsoft Certified professional. To ensure the quality of this micro-credential, continuous feedback loops with students, instructors, and industry practitioners are maintained to continually improve content, delivery, and assessment methods.

Learning Pathways

Your journey into Machine Learning begins with either Data Engineering or Data Management. This foundational knowledge will equip you with the skills needed to explore the exciting field of Machine Learning, which, in turn, opens doors to the broader field of Data Science.