Support

If you’re having problems with the platform, you can reach out to our IT support team through the live chat.

Skip to main content

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.

Is this course for you?

Whether you are a data expert, a student of computer science or engineering, a freelancer or just someone who wishes to learn about Machine Learning, then this course is for you!

This machine learning course strikes the right balance between both theory and real-world applications of Machine Learning. The course also explores ethical dilemmas around the use of AI and how Machine Learning can be utilized for social good. Given the local context, the videos use a mix of English and Urdu to create a more accessible learning environment.

If you are looking to expand your skill set, apply for graduate school, progress in your career, or unlock better freelance opportunities, then this is the best machine learning course online that will equip you with the necessary knowledge needed to step into the exciting world of Machine Learning.

To take this course, you need to know at least one programming language (Python, R, C or C++, SQL or any other) and be familiar with foundational concepts of Probability, Statistics, and Linear Algebra.

What will you learn?

Course Objective: Ignite enthusiasm for Machine Learning and equip learners with the foundational skills to harness its potential.
By the end of this machine learning course, learners will be able to:

  • Intuitively grasp the core principles behind Machine Learning models, tools, and methodologies.
  • Master the mathematical underpinnings of statistical learning.
  • Rigorously navigate the lifecycle of designing, executing, and assessing key Machine Learning models.
  • Select the optimal algorithm for specific challenges and discern the merits and limitations of each.
  • Comprehend the holistic integration of ML in application areas, spanning data sourcing, annotation, algorithm selection, societal biases, model explainability, and its transformative implications.
  • Time Duration 6 hrs per week (3 months)
  • Difficulty level Intermediate
  • No classes required 100% Online
  • Prerequisites None
  • Language English
  • Self-Paced
  • Full Lifetime Access

Offered By

LUMSx

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.

Instructors

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

Outline

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

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

Show breakdown

Enrollment Options

Free Plan Verified Plan
Price Free PKR 11,999
ilmX support
Shareable certificate upon completion
Graded assignments and exams
Get Started for Free Get Full Access

Shareable Certificate

Upon completion of the course, you receive a signed certificate from the institute. You can share this machine learning course certificate in the certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

lums

Frequently Asked Questions (FAQs)

Instalments

You can purchase this course by paying the amount in two instalments.

  1. Sign up on the ilmX platform from this link.
  2. Next, send an email to support@ilmx.org with the subject "Applying for Instalments". Please mention the course name you are interested in purchasing and your email id that you used to register on the platform.
  3. Our team will get back to you in 48hrs. They will share bank account details and the first instalment amount.
  4. Make the payment and share the bank receipt screenshot in the same email thread.
  5. If the course has a free track then you will be enrolled in that track and given full access to the course once you make the second payment within 30 days.
  6. If the course does not have a free track then you will be given access to the course once you make the second payment within 30 days.

Note: Please make sure to pay the second instalment no later than 30 days.

Student Discount

If you are a student then you can avail 15% discount on this course. if a discount is already running on the course then this discount will be applied on the discounted course price.

  1. Sign up on the ilmX platform from this link.
  2. Next, send an email to support@ilmx.org with the subject "Student Discount". Please mention the course name you are interested in purchasing, your email id that you used to register on the platform, your CNIC, your most recent semester challan fee, and your university/school ID.
  3. Our team will get back to you in 48hrs after confirming your details. They will share bank account details and the discounted amount that you have to pay for the course.
  4. Make the payment and share the bank receipt screenshot in the same email thread.
  5. Within 24hrs of confirmation, our team will enroll you in the course and you will receive an enrollment email.

Note: You can avail this discount even if you have opted for instalment payment method. This discount is not offered during Pre-launch Sale.

LUMS Staff Discount

If you are a member of the LUMS staff you can avail 10% discount on this course. if a discount is already running on the course then this discount will be applied on the discounted course price.

  1. Sign up on the ilmX platform from this link.
  2. Next, send an email to support@ilmx.org with the subject "LUMS Staff Discount". Please mention the course name you are interested in purchasing, your email id that you used to register on the platform, and a picture of your LUMS employee ID card.
  3. Our team will get back to you in 48hrs after confirming your details. They will share bank account details and the discounted amount that you have to pay for the course.
  4. Make the payment and share the bank receipt screenshot in the same email thread.
  5. Within 24hrs of confirmation, our team will enroll you in the course and you will receive an enrolment email.

Note: You can avail this discount even if you have opted for instalment payment method. This discount is offered during Pre-launch Sale.

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
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.
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.
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.
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.

For the first programming assessment, the tool will automatically assign your work to be assessed by one of your peers after which it will be assessed/graded by a staff member. In this assessment, you will also be expected to assess the work of one of your peers against a given rubric. Peer grading gives you an opportunity to provide and receive feedback from your fellow learners to further improve your concepts and skills.

As you progress through the course, the programming assessments will increase in level of difficulty. To ensure you are applying your knowledge correctly, the rest of the programming assessments will only be graded by an expert staff member.

Your final grade in all the programming assessments will be determined by the grading done by the staff member.
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 support@ilmx.org or use the chat widget tool available on the platform for the ilmX team to address your query.
Yes, you have to watch all the videos to complete this course.

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.

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.
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.
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.
Yes, you will have continued access to the course materials and resources even after completing the course.
You will need to go through all the units and attain a 55% grade in assessments to pass the course and get a certificate.
Please forward any queries to our team on the chat widget or email your query to us at support@ilmx.org. We will only be responding to technical support queries. Content related queries cannot be entertained at the moment.
Enroll