- Machine Learning
- Advanced Machine Learning
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Are you ready to take your Machine Learning journey to the next level? Building on foundational concepts, this course dives into advanced machine learning techniques that power contemporary AI systems across a wide range of industries—from fraud detection and recommendation engines to autonomous systems and robotics.
In this second course of the Machine Learning series, you will explore more sophisticated and versatile models such as decision trees, ensemble models, clustering algorithms, anomaly detection, and reinforcement learning. Through a blend of theory and hands-on practice, you will gain the ability to uncover hidden patterns in data, reduce its dimensionality with PCA, and make data-driven decisions in uncertain environments.
Whether you are looking to switch careers or aiming to enhance your current career by bringing smarter AI systems to life, this course equips you with the tools and insights to easily move from learning models to mastering them. With carefully designed modules and programming assessments, captivating videos which will keep you hooked at all times, and rapid staff feedback, this course will strengthen your foundations in both unsupervised and supervised paradigms of machine learning, enabling you 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 seasoned machine learning practitioner!
By the end of this machine learning course, learners will be able to:
This course is part of the Advanced Track in the Data Science Specialization. “Learn More” about how to enroll in the specialization
Welcome to the course of Advanced Machine Learning! In this module we will dive right into Decision Trees. Learn how machines make decisions using tree structures, understand concepts like entropy and information gain, and build interpretable models for both classification and regression tasks.
This module will further build on your concepts of Decision Trees and explore how multiple weak models can combine their predictive powers to become amazingly powerful and accurate models! Explore powerful techniques like bagging, random forests, and boosting that combine multiple models to improve performance and reduce overfitting
Unsupervised Learning methods form some of the core techniques in Machine Learning. This module will equip you with the foundational knowledge and practical skills necessary to apply unsupervised learning algorithms to real-world problems. Discover how to find patterns in unlabeled data using clustering techniques like K-Means and Gaussian Mixture Models, along with hierarchical methods.
In this module we will expand our toolkit for Unsupervised Learning methods. Reduce data complexity and enhance model performance using dimensionality reduction techniques like Principal Component Analysis (PCA). Learn the mathematics behind the algorithm in a step-by-step manner, and how to incorporate dimensionality reduction techniques with supervised learning methods as well.
In this module you will learn to identify rare and unusual data points by applying unsupervised and supervised techniques that help flag outliers in complex datasets. Learn about the real world use cases of this technique and the key role it plays in fraud detection across different financial sectors.
In this module you will began by understanding how certain AI bots have become capable in beating world-renowned chess grandmasters, as well as in other games. Understand how agents learn optimal behavior by interacting with their environment using rewards and penalties in sequential decision-making settings.
Upon completion of the course, you receive a signed certificate from the institute. You can share this certificate in the certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
When you click on the ‘Enroll Now’ button, you will be asked to register online. Once you have completed your online registration, you will proceed to the payment section where you can choose from three options:
1. Pay via bank: You will be able to instantly download a fee voucher for hassle-free bank deposits. After your payment is confirmed, you will receive an email within 24 hours, granting you access to our learning management system.
2. Pay online: By opting for ‘Pay Online’, a voucher will be automatically generated with a simple click to swiftly complete the payment. After your payment is confirmed, you will receive an email within 24 hours, granting you access to our learning management system.
3. Pay in instalments: LUMSx is an official partner of the KalPay Taleem team, which offer an instalment facility for learners. If you would like to avail this option, please complete the online registration form and select the option ‘Pay in instalments’. For more information, contact taleem@kalpayfinancials.com or call at 0328 3044414
This course is ideal for learners who have a foundational understanding of machine learning and are eager to deepen their knowledge by exploring more advanced concepts and real-world applications. If you’ve completed our Machine Learning course or are familiar with core supervised learning methods such as linear regression, logistic regression, and basic neural networks, this course will build on that knowledge and expand your skill set.
It is particularly suited for undergraduate students pursuing AI and data science, alongside professionals who may want to enhance their portfolio and skillset with advanced unsupervised and supervised learning methods.
Whether you’re aiming for a career in AI or simply curious about how intelligent systems learn and adapt, this course offers the advanced tools and insights needed to thrive in today’s rapidly evolving ML landscape.
To take this course, learners are required to take the Machine Learning course. They should also be sufficiently comfortable with Python, and basic theory 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.
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.
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.
The Machine Learning course serves as an introductory foundation, focusing on the core concepts and techniques in supervised learning. It covers essential algorithms such as KNNs, Naive Bayes, linear regression, logistic regression, and neural networks, while also introducing fundamental ideas like loss functions, overfitting, and evaluation metrics. This course is designed to build strong intuition and mathematical understanding for beginners entering the field.
In contrast, the Advanced Machine Learning course builds upon this foundation by exploring more complex and diverse ML paradigms, introducing unsupervised and reinforcement learning methods, in addition to supervised models.
No, the learners cannot take the advanced machine learning course without taking the machine learning course.
Questions? Email us at contactlumsx@lums.edu.pk
or call us on +92 42 3560 8000 | Ext: 8567 or 0321-0667775
