This course will introduce you to statistical analyses, mathematical modelling, probability, and optimization techniques, Supervised and unsupervised learning models, advanced machine learning applications, deep learning concepts and applications, etc. You will gain hands-on experience working with open-source...
Syllabus WEEK 1 The Foundational Underpinnings of Machine Learning In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? This module covers the fundamental ways in which machine learning works – and doesn't work...
Lounis H, Ait-Mehedine L (2004) Machine-learning techniques for software product quality assessment. In: Fourth international conference onquality software, 2004. QSIC 2004. Proceedings. IEEE, pp. 102–109 Marill KA (2004) Advanced statistics: linear regression, part II: multiple linear regression...
This course in the Coursera catalog offered and delivered by IBM is a rich syllabus covering all the vital areas of machine learning, like deep learning and reinforcement learning. This course is intended for students interested in pursuing a career in Machine learning. The course is a six (6)...
The course is designed to provide a solid foundation in machine learning algorithms, data analysis, and model evaluation. Some of the learning objectives for this course include: Gain a deep understanding of various machine learning algorithms, including supervised and unsupervised learning techniques. ...
Get information about online machine learning courses & certifications eligibility, fees, syllabus, admission, scholarship. Know complete details of admission process, scope & career opportunities, placement & salary package.
Machine Learning internship india 2021 – Syllabus 100% Practical – Live HandsOn – Data science Machine learning Internship Topic 1 :Machine Learning– Introduction What is Machine Learning , Linear Regression Theory , Multiple Linear Regression , Decision Tree , Naive Bayes classifiers, Support Vecto...
Partner with one studentand select a machine learning problem of your choice. Apply the machine learning techniquesyou’ve learned during the course toyour chosen problem. Present your projectto the class at the semester’s end. Submission Requirements on GradescopeSubmit the following onGradescopeby ...
Soon, machine learning techniques will enable fully automated driving, the creation of better materials, and even the discovery of new medical treatments. Personalized experiences For some time now, social media apps have been using machine learning to curate content tailored to individual users. But...
In the exercise hours, the students will apply the studied machine learning techniques to solve sample problems in practice. SYLLABUS Types and sample application of machine learning Bayesian learning Instance based learning Genetic algorithms Neural networks Kernel methods Combining multiple learners ...