Learn to apply popular machine learning algorithms such as linear regression, logistic regression, k-means clustering, artificial neural networks (ANNs), and support vector machines by building a movie recommendation system to get hands-on experience with these methods.
In this example, the average classification error (SE) is relatively minimal, and all class probabilities closely approximate their expected values for all categories a through g.
Machine learning is the study of using algorithms to identify patterns and relationships within data automatically, and this course introduces you to its many types, such as linear regression with one and multiple variables, neural networks, unsupervised learning, and regularization techniques. Furthermore, this course also serves as an overview of statistical methods, probability theory, and optimization techniques.
Learn to prepare datasets for machine learning analysis, evaluate and compare various ML models’ performances, and use machine learning tools to solve real-world issues such as movie recommendation systems and fraud detection algorithms. By the end of this course, you’ll have acquired all of the principles behind machine learning to apply it effectively to your projects and make informed decisions when selecting an ideal model for your data.
No matter who you are: software developer, statistician, experienced applied mathematician, or student looking to become a machine learning scientist, this course has something for you. By the end, you’ll leave with more excellent knowledge of Regression and Classification algorithms as well as an understanding of how ML can be applied to complex problems; additionally, using Python’s Machine Learning Toolkit, you will have built your models with machine learning and be given an easily shareable certificate of completion!
This course introduces the concepts and algorithms used in machine learning, including regression, clustering, classification, and information retrieval. You will gain an in-depth understanding of data analysis and selecting an appropriate algorithm for specific problems.
This course is a rigorous introduction to machine learning, covering every aspect of this branch of science. This program is designed to equip participants with the theoretical and practical experience necessary for becoming machine learning engineers or scientists; this program targets software developers, statisticians experienced applied mathematicians, and other scientists interested in becoming machine learning specialists.
We present the LUM family of large-margin classifiers, which bridge soft and hard classification. This unique computational algorithm enables one to use it across a spectrum of quiet to hard sort. Furthermore, LUMs produce class probabilities as by-products, allowing us to compare and evaluate various classes on one problem.
This course covers every facet of supervised and unsupervised learning, from building movie recommender systems to popular algorithms like boosting and regularization. By the end of this course, you will have acquired all the skills needed to build intelligent applications using your models and algorithms.
This course explores both the theoretical and practical aspects of Machine Learning, covering topics such as supervised, unsupervised, reinforcement learning algorithms, regression, classification, and becoming acquainted with significant machine learning libraries in Python. Furthermore, students will implement various simple algorithms themselves.
Kristian Lum is an enthusiastic statistician interested in finding patterns to explain real-world phenomena. I am currently the lead statistician at HRDAG and previously worked as a research assistant professor at Virginia Bioinformatics Institute. Her passion lies in teaching others complex topics.
LUMS is a non-profit federal university chartered by the Government of Pakistan and exempt from paying income tax under Section 61 of the Income Tax Ordinance 2001. Donations made to LUMS qualify as tax deductions. LUMS provides undergraduate and graduate degrees in Computer Science, Artificial Intelligence, Data Science, Computer Vision and Machine Learning. Furthermore, the institution houses research centers specializing in Big Data Analytics, Cyber Security, and Natural Language Processing. FutureTech at LUMS provides high school students interested in AI and Machine Learning an intensive one-week summer learning boot camp on its Karachi campus to foster future technologists who will shape the future.
LUMS is Pakistan’s premier university, ranks number one for applied mathematics nationally, and is featured among the world’s 700 universities. Offering both undergraduate and graduate degrees with solid global links, its graduates are highly sought after in both local and international job markets; additionally, various summer learning programs, including FutureTech, are available through this institution.
This course will introduce the fundamentals of machine learning using MATLAB and teach how to apply them to real-world problems. You will explore both supervised and unsupervised learning algorithms and improve model performance using principal component analysis and regularization to reduce the complexity of models. Finally, you will build a movie recommender system while gaining experience in data science through machine learning – culminating with awarding a shareable certificate upon completion!
The LUMS method offers a natural transition between soft and hard classification. It can accommodate various classifiers while producing class probability estimation as a byproduct, greatly simplifying systematic exploration and comparison among methods. We analyze both the theoretical consistency and numerical performance of LUMs on simulated examples; specifically, their Fisher consistency scores can be tuned up for better class probability estimation performance.