Course Introduction
This course, titled Machine Learning for Beginners, is a comprehensive and foundational program designed to introduce learners to the core principles, techniques, and workflows of classical machine learning. Throughout the course, students will explore essential ML concepts such as regression, classification, data analysis, performance evaluation, and model building using Python-based tools. It is tailored for aspiring developers, data analysts, computer science students, and professionals seeking to build a strong foundation in machine learning and practical ML model development.
This course provides a carefully structured learning path that integrates conceptual understanding with practical implementation using Jupyter Notebooks inside Visual Studio Code. Learners will acquire hands-on experience working with widely used libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn, preparing them to build, evaluate, and deploy real-world machine learning models.
Course Duration and Modules
Overall Duration:
The course is designed to be completed over 4–6 weeks, depending on the learner’s pace.
Total Video Duration:
The combined duration of all course videos is approximately 1 hour and 12 minutes.
Total Learning Hours:
Learners are expected to spend 18–25 total learning hours, including:
- 8 hours of video and guided content
- 10–17 hours of hands-on exercises, coding practice, and self-study
Weekly Commitment:
Learners should invest 4–6 hours per week to complete the course comfortably.
Pacing:
This course is self-paced, allowing learners to progress according to their own schedule. However, completing at least three videos per session is recommended to maintain continuity.
Course Modules & Video Lecture Titles
Module 1 — Foundations of Machine Learning
- Introduction to Machine Learning for Beginners
- The History of Machine Learning
- Techniques for Machine Learning
- Setup Your Tools for Machine Learning
Module 2 — Regression Techniques
- Introduction to Regression Models
- Set Up Jupyter Notebooks
- Your First Linear Regression Project
- How to Analyze and Clean a Dataset
- How to Visualize Data with Matplotlib
- Understanding Linear Regression
- Looking for Correlation
Module 3 — Advanced Regression & Classification
- Linear and Polynomial Regression Using Scikit-Learn
- Categorical Feature Predictions
- Understanding Logistic Regression
- Data Analysis for Logistic Regression
- Logistic Regression for Classification
- Analyzing Logistic Regression with ROC Curves
Course Presenter
This course is presented by the Microsoft Developer Education Team, a group of expert instructors with extensive experience in artificial intelligence, data science, and software engineering. The presenters are known for their clear, practical, hands-on teaching style that bridges theory and application. Their content is highly structured, beginner-friendly, and aligned with industry best practices.
Course Certificate
The Qalam Scholar Certificate awarded upon successful completion of this course holds international recognition and is equipped with a barcode verification system. This ensures authenticity and enhances your professional credibility, supporting both national and global career opportunities in data science and AI-related fields.
Learning Objectives
By the end of this course, students will be able to:
- Explain core machine learning concepts, terminology, and techniques.
- Set up a complete ML development environment using VS Code and Jupyter Notebook.
- Analyze, clean, and visualize datasets for machine learning purposes.
- Build, train, and evaluate regression and classification models using Scikit-Learn.
- Apply logistic regression for binary classification tasks.
- Interpret model outputs using metrics such as correlation and ROC curves.