Search result for Mathematics for machine learning: linear algebra Online Courses & Certifications
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Statistics Primer for Data Scientist's
by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!- 4.2
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3 hours on-demand video
Statistics for Data Science 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. Machine Learning...
$12.99
Exploratory Data Analysis for Machine Learning
by Mark J Grover , Miguel Maldonado- 4.6
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Approx. 8 hours to complete
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting. Introduction to Artificial Intelligence and Machine Learning Machine Learning and Deep Learning...
Sparse Representations in Signal and Image Processing: Fundamentals
by Michael Elad , Alona Golts- 0.0
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5 Weeks
Its core idea is the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements. A series of theoretical problems arise in deploying this seemingly simple model to data sources, leading to fascinating new results in linear algebra, approximation theory, optimization, and machine learning....
$149
Specialized Models: Time Series and Survival Analysis
by Mark J Grover , Miguel Maldonado- 4.5
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Approx. 11 hours to complete
Identify types of problems suitable for survival analysis To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics. Check for Understanding...
Advanced Machine Learning and Signal Processing
by Romeo Kienzler , Nikolay Manchev- 4.5
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Approx. 27 hours to complete
We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. Linear algebra Unsupervised Machine Learning Machine Learning Supervised Machine Learning Linear Regression Linear Regression using Apache SystemML Linear Regression Unsupervised Machine Learning...
Unsupervised Learning
by Mark J Grover , Miguel Maldonado- 4.9
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Approx. 9 hours to complete
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting....
Unsupervised Machine Learning
by Mark J Grover , Miguel Maldonado- 4.8
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Approx. 9 hours to complete
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting....
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AI Workflow: Business Priorities and Data Ingestion
by Mark J Grover , Ray Lopez, Ph.D.- 4.3
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Approx. 8 hours to complete
Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. Describe the use case for sparse matrices as a target destination for data ingestion Process Models & Design Thinking: Check for Understanding...
Convex Optimization
by Stephen Boyd , Henryk Blasinski- 0.0
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8 Weeks
The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learn He has served as a TA for EE364a at Stanford....
Convex Optimization
by Stephen Boyd , Henryk Blasinski- 0.0
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8 Weeks
The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance....
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