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Machine Learning Algorithms: Supervised Learning Tip to Tail
by Anna Koop- 4.7
Approx. 9 hours to complete
This course takes you from understanding the fundamentals of a machine learning project. You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute. Scikitlearn documentation for linear regression (Optional)...
Computing for Data Analysis
by Richard (Rich) Vuduc- 0.0
15 Weeks
For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. The hands-on component of this course will develop your proficiency with modern analytical tools....
$825
Machine Learning Prerequisites for 2021
by Pythonist org- 4.3
1.5 hours on-demand video
Learn the foundation and prerequisites to become a Machine Learning Engineer In this course, you are going to learn the prerequisites for machine learning. Machine Learning is a vast subject that involved various other fields like Mathematics and Statistics which makes it complex....
$12.99
Robotics: Aerial Robotics
by Vijay Kumar- 4.5
Approx. 18 hours to complete
Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Dynamics and 1-D Linear Control...
Matlab and Octave in Jupyter Notebook
by Sourabh Sinha- 3.9
2 hours on-demand video
Linear Algebra using Matlab/Octave...
$12.99
AI Workflow: Business Priorities and Data Ingestion
by Mark J Grover , Ray Lopez, Ph.D.- 4.3
Approx. 8 hours to complete
This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. By the end of this course you should be able to: Course Introduction About this Course...
Case Studies in Functional Genomics
by Rafael Irizarry , Michael Love , Vincent Carey- 0.0
5 Weeks
The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples. 2x: Introduction to Linear Models and Matrix Algebra...
$149
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Statistics Primer for Data Scientist's
by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!- 4.2
3 hours on-demand video
Statistics, Math, Linear Algebra If we talk in general about Data Science, then for a serious understanding and work we need a fundamental course in probability theory (and therefore, mathematical analysis as a necessary tool in probability theory), linear algebra and, of course, mathematical statistics....
$12.99
Quantum Information Science II: Advanced quantum algorithms and information theory
by Isaac Chuang , Aram Harrow- 0.0
4 Weeks
3x course module draws upon quantum complexity and quantum information theory, to cover in depth advanced quantum algorithms and communication protocols, including Hamiltonian simulation, the hidden subgroup problem, linear systems, and noisy quantum channels. Knowledge of linear algebra is also strongly recommended, and other helpful math topics to know include probability and finite fields....
$49
Data for Machine Learning
by Anna Koop- 4.4
Approx. 12 hours to complete
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). Introduction to the Course...