Machine Learning - Ilmx

About this Course

Are you intrigued by the world of Artificial Intelligence (AI) and wish to learn about the fascinating field of Machine Learning (ML)? With applications ranging from self-driving cars to voice assistants and large language models, Machine Learning is revolutionizing the way we interact with the world at a fast-evolving pace.

This course is designed to equip you with the essential skills, concepts, and applications of machine learning, setting you on the path to becoming proficient in this field. In this structured and immersive course, you'll go through fundamental concepts to advanced techniques, guided by a logical progression through eleven well-curated modules. Whether you are diving into the nuances of supervised learning, grasping the principles behind neural networks, or exploring the ethical dilemmas encompassing AI and ML, this course provides a comprehensive learning experience.

With captivating videos, hands-on exercises, and peer and staff feedback, you will be able to apply machine learning concepts to real-world scenarios. By the end of this course, you'll not only have a deep understanding of machine learning techniques but also know how to leverage them responsibly and ethically in various fields.

This course is led by Dr. Agha Ali Raza, known for his stimulating teaching style and ability to deconstruct some of the most complex ML algorithms into everyday, applicable concepts. Let's embark on this enriching learning journey together, paving your way to becoming a proficient machine learning practitioner!

Is this course for you?

A higher education instructor/practitioner, trainer, training designer, curriculum developer, K- 12 school teacher, teaching assistant, or anyone seeking to create a rich and impactful learning environment for their learner: if you belong to one of these roles or aspire to pursue them, this course is meant for you!

This comprehensive journey will take you from the foundational steps of crafting learning outcomes to designing assessments and guide you in discovering suitable tools and technologies to enhance your course using the constructive alignment approach. Whether you're just starting out or looking to improve your teaching practices, this course will equip you with the knowledge and skills to create engaging and compelling learning experiences.

What will you learn?

  • Explain how learning works and is supported through a constructively aligned course. 
  • Write effective and measurable learning outcomes for your course.
  • Develop a learner-centered assessment plan that aligns with course learning outcomes.
  • Develop teaching and learning activities that align with the course's assessments and learning outcomes.
  • Identify meaningful ways to integrate technology into a course to support learning.
  • Identify and describe the components of a learner-centered course outline.
  • Time Duration 6 hours per week
  • Difficulty level Intermediate
  • No classes required 100% Online
  • Prerequisites None
  • Language English
  • Self-Paced
  • Full Lifetime Access

Offered By


LUMSx is the center for online learning and professional development at LUMS. It extends LUMS’ excellence in teaching and research beyond its borders by leveraging technology and innovative pedagogy.


  • lums
    Dr. Agha Ali Raza Assistant Professor of Computer Science School of Science and Engineering LUMS

Module 0: Welcome to Machine Learning

    Welcome to the course on Machine Learning! In this module you will learn about what Machine Learning is? Who is this course for? What this course contains and how will you be able to benefit from this course. This introductory module will give you information on the instructor’s profile, course syllabus and objectives, different features of the course, grading policies, expectations around academic honesty, frequently asked questions, and a chance to chat with your peers.

  • 10 units
  • 1 hour

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Module 1: Introduction to Machine Learning

This module will uncover the wonderful world of machine learning, demonstrating its ubiquity in our lives and explaining its underlying concepts. Through a mix of theory and examples, this module will give you a comprehensive understanding of machine learning's key concepts, historical background, applications, challenges and how it can be harnessed for social good. The module will also give you an opportunity to learn the basics of python and apply them through a programming assessment.

  • 13 units
  • 5 hours

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Module 2: Supervised Learning

Supervised learning is one of the fundamental techniques in Machine Learning. This module will equip you with the foundational knowledge and practical skills necessary to apply supervised learning algorithms to real-world problems. Through a combination of theoretical concepts and hands-on exercises, you will gain a solid understanding of the principles, algorithms, and evaluation methods involved in supervised learning.

  • 21 units
  • 2 hours

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Module 3: KNN

K-NN is a non-parametric method used for both classification and regression tasks. This module will familiarize you with the underlying principles, implementation, and evaluation of the K-NN algorithm. Through theoretical explanations and practical examples, you will gain proficiency in applying K-NN to real-world problems, selecting an appropriate value for K, handling distance metrics, dealing with imbalanced data, and optimizing model performance.

  • 32 units
  • 3 hours 30 minutes

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Module 4: Evaluation of Classifiers

The module provides a comprehensive understanding of essential evaluation metrics for classification tasks. You will begin with learning about accuracy, build up to precision, recall, and F1-score, which are widely used performance measures that assess the effectiveness of classifiers in predicting class labels. This module will equip you with the knowledge and skills to calculate and interpret these metrics accurately. You will gain a solid understanding of the concepts behind precision (the proportion of correctly predicted positive instances), recall (the proportion of actual positive instances correctly predicted), and F1-score (a harmonic mean of precision and recall). Through practical examples and exercises, you will learn how to apply these metrics to assess classifier performance and make informed decisions based on their results

  • 39 units
  • 7 hours

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Module 5: Linear Regression

In this module, you will gain a comprehensive understanding of linear regression, a widely-used technique in predictive modeling. You will learn the fundamental principles and assumptions of linear regression, including linearity and independence. The module will also focus on parameter estimation, coefficient interpretation, and prediction. Additionally, important topics like regularization techniques will be explored. Through hands-on exercises and real-world datasets, you will develop practical skills in building, evaluating, and improving linear regression models, enabling you to analyze data, make accurate predictions, and extract valuable insights.

  • 35 units
  • 8 hours

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Module 6: Logistic Regression

Logistic regression is a powerful tool used to predict the probability of a binary outcome based on a set of input variables. In this module you will cover the underlying concepts and assumptions of logistic regression, including the logistic function and loss function. You will also explore the process of model fitting, parameter estimation, and interpretation of results. Practical examples and hands-on exercises are included to enhance your understanding and application of logistic regression in real-world scenarios. By the end of the module, you will have a solid foundation in logistic regression and you will be equipped to utilize this technique for predictive modeling and decision-making tasks.

  • 23 units
  • 5 hours 30 minutes

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Module 7: Neural Networks

The module on Neural Networks provides you with an introduction to this powerful machine learning technique that mimics the structure and functioning of the human brain. Neural networks are composed of interconnected nodes, or artificial neurons, organized in layers that process and transform data. Here you will cover the fundamental concepts and components of neural networks, including activation functions, weight initialization, forward and backward propagation, and gradient descent optimization.

  • 33 units
  • 9 hours

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Module 8: Support Vector Machines

The module on Support Vector Machines (SVM) offers an introduction to this powerful supervised learning algorithm used for classification and regression tasks. SVMs aim to find the optimal hyperplane that separates data points of different classes with the largest margin. In this module you will learn about the underlying principles of SVM, including the concept of support vectors, kernel functions, and the margin optimization objective. You will explore both linear and nonlinear SVMs, highlighting their strengths and limitations.

  • 9 units
  • 1 hour

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Module 9: Bayes Theorem

This module provides you with an introduction to Bayes Theorem, a fundamental concept in probability theory and statistics. Bayes Theorem allows us to update our beliefs about the probability of an event based on new evidence or information. The content sheds light on the core components of Bayes Theorem, including prior probabilities, likelihoods, and posterior probabilities. It also explores how Bayes Theorem can be applied to various scenarios, such as medical diagnostics and spam filtering.

  • 18 units
  • 1 hour 30 minutes

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Module 10: Naive Bayes Classifier

This module introduces the Naive Bayes classifier, a simple yet effective probabilistic algorithm used for classification tasks. The Naive Bayes classifier is based on Bayes' theorem and makes the assumption of independence among features. Here, you will cover the key concepts and workings of the Naive Bayes classifier, including the calculation of prior probabilities, likelihoods, and posterior probabilities.

  • 15 units
  • 5 hours

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Module 11: Responsible AI and Machine Learning for Development

This module aims to unveil the 'black box' nature of artificial intelligence and machine learning models, enabling deeper understanding of their inner workings and addressing the multifaceted issues related to AI ethics, fairness and explainability. It covers fairness in AI, interpretability of ML models, sources of bias and techniques to mitigate bias. The module also touches upon ethics in AI to understand the moral principles guiding AI development and its use. Lastly, the content covers machine learning for development, explaining how ML techniques can be used to address social and economic challenges in developing countries.

  • 24 units
  • 2 hours 30 minutes

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Let's talk about how ilmX can transform your organization

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Frequently Asked Questions (FAQs)

  • Who is this course designed for?
    The course is suitable for data experts, freelancers, students of computer science or engineering, or anyone who wants to learn about Machine Learning. To take this course, you need to know how to program in at least one programming language (Python, R, C or C++, SQL or any other) with knowledge of Probability, Statistics and Linear Algebra,
  • Do I need to have prior knowledge of programming to take this course?
    Yes. You need to know how to program in at least one programming language (Python, R, C or C++, SQL or any other). Although the course uses Python for the Programming Assessments, you do not need to know programming in Python to start this course. You will learn the necessary rules of Python as you progress through the course.
  • Are there any prerequisites for this course?
    Yes. To take this course, you need to know how to program in at least one programming language (Python, R, C or C++, SQL or any other) with knowledge of Probability, Statistics and Linear Algebra.
  • .What is the duration of this course?
    This is a self-paced course. The recommended duration to complete the 51 hours of course material is two and a half to three months (approximately 6 hours of effort per week). The course consists of engaging learning materials and interactive activities that will guide you through the course journey.
  • How will programming assessments be graded?
    In this course, you will be using a Peer Assessment Tool to submit your programming assessments. The tool uses a combination of peer and staff grading mechanisms. After submitting your work, the tool will automatically assign it to be assessed by 2 of your peers after which it will be assessed by a staff member. Peer grading gives you an opportunity to provide and receive feedback from your fellow learners to further improve your concepts and skills. Your final grade will be determined by the grading done by the staff member.
  • The peer or staff is taking too long to grade my programming assessment. What should I do?
    This is an asynchronous course, and each learner will be progressing through the course at their own pace, you may have to wait for your peers to review your response. Similarly, it may take some time for a staff member to review and grade your work. While you await their responses, you can move ahead in the course. In the event that you do not receive a grade from your peers or staff for more than two weeks, please reach out to the ilmX support team at or use the chat widget tool available on the platform for the ilmX team to address your query.
  • Do I have to watch all the videos to complete the course?
    Yes, you have to watch all the videos to complete this course.
  • Is this course self-paced or instructor led?
    This course is self-paced, allowing you to learn at your own convenience. However, there are certain components that allow you to interact with your peers through the discussion forums and peer grading on programming assessments. To allow you and your peers to progress through the course without extensive delays, it is recommended that you give timely feedback to your peers on the programming assessments.
  • What resources and materials will be provided in the course?
    The course will provide you with a variety of resources, including instructional videos, additional readings, practical examples, quizzes, programming assessments and discussion forums. These resources will support your learning journey and help you apply the concepts you have learned in the course.
  • Can I collaborate with other learners in this course?
    While discussion and collaboration with peers is encouraged to foster a learning community, sharing or copying code/solutions is strictly prohibited. Any collaboration should be limited to discussing concepts and should not involve sharing actual code or solutions.
  • What should I do if I am away for a few days?
    Your progress for the course is always saved in your ilmX account. Whenever you log in again, you will be able to proceed from where you left off.
  • Will I have access to the course materials after completing the course?
    Yes, you will have continued access to the course materials and resources even after completing the course.
  • What is the passing grade to get the certificate?
    You will need to go through all the units and attain a 55% grade in assessments to pass the course and get a certificate.
  • Who should I contact if I have additional questions?
    Please forward any queries to our team on the chat widget or email your query to us at We will only be responding to technical support queries. Content related queries cannot be entertained at the moment.