🎓 Course Introduction:
This course, titled “Machine Learning for Beginners,” is a comprehensive program designed to introduce learners to the core concepts, principles, and tools of classical machine learning (ML). Throughout the course, students will explore foundational topics such as linear regression, logistic regression, data cleaning, visualization, and model evaluation using real-world examples and practical exercises. The course is tailored for students, professionals, and enthusiasts who wish to build a solid foundation in machine learning and apply analytical thinking to solve complex data problems. With step-by-step tutorials and guided coding sessions, learners will gain both theoretical understanding and hands-on experience in ML model development.
👨🏫 Course Presenter:
The course is presented by Career Certificate (Microsoft Developer), a trusted name in digital education and professional certification. With a proven record in technology training, the presenter combines clarity, practical coding demonstrations, and an interactive teaching style to ensure that even complete beginners can follow along easily. Their engaging instructional videos and structured learning path enable students to progress from basic concepts to implementing their own models using Python and libraries such as Scikit-Learn, NumPy, Pandas, and Matplotlib.
🏅 Course Certificate:
The Qalam Scholar Certificate offered upon completion of this course holds international recognition and features a unique barcode for verification.
This certificate validates your skills in machine learning and strengthens your credibility in both academic and professional environments. It supports your journey toward national and international career advancement in data science, AI, and analytics.
🎯 Learning Objectives:
By the end of this course, students will be able to:
· Understand the fundamental principles of classical machine learning.
· Differentiate between supervised and unsupervised learning techniques.
· Build, train, and evaluate linear and logistic regression models using Scikit-Learn.
· Analyze and visualize datasets using NumPy, Pandas, and Matplotlib.
· Prepare and clean datasets for ML modeling.
· Apply machine learning concepts to solve real-world prediction and classification problems.