Search result for Machine learning and linear algebra Online Courses & Certifications
FollowFilter ![]()
Filter ![]()
Showing 321 courses
By Category ![]()
By Subject![]()
By Language![]()
By Duration![]()
By Price![]()
Get Course Alerts by Email
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 34 hours to complete
The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. What Deep Learning is and is not...
Data Science Fundamentals for Data Analysts
by Emma Freeman , Mark Roepke- 0.0
-
![]()
![]()
![]()
![]()
![]()
Approx. 19 hours to complete
Supervised and Unsupervised Learning Basics of Machine Learning Practical Machine Learning A Review of Supervised Learning and Regression Linear Regression Machine Learning Solutions Discussion Intro Measuring Success and Constraints Machine Learning Solutions Apply foundational data science concepts and techniques to solve these real-world problems. Design, execute, assess, and communicate the results of your very own data science projects....
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
by Eric Siegel- 4.8
-
![]()
![]()
![]()
![]()
![]()
Approx. 17 hours to complete
The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. Machine learning software: dos and don'ts for choosing a tool Machine learning software: how tools vary and how to choose one Machine learning software: dos and don'ts for choosing a tool Machine learning software: how tools vary and how to choose one...
Project Planning and Machine Learning
by David Sluiter- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 17 hours to complete
* Why we want to study big data and how to prepare data for machine learning algorithms Machine Learning Segment 4 - Machine Learning Schools of Thought Segment 6 - Categories of Machine Learning Segment 20 - Machine Learning in IIoT Segment 21 - Machine Learning Summary Segment 6 - Machine Learning, Generalization and Discrimination...
Introduction to numerical analysis
by Evgeni Burovski- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 18 hours to complete
Numerical computations historically play a crucial role in natural sciences and engineering. As prerequisites for this course, we assume a basic command of college-level mathematics (linear algebra and calculus, mostly), and a basic level of programming proficiency. Simple geometric quadratures: Trapezoids, Simpson's rule and all that. Approximation and convergence. Linear Multistep methods....
Mathematics for Machine Learning: PCA
by Marc Peter Deisenroth- 4
-
![]()
![]()
![]()
![]()
![]()
Approx. 18 hours to complete
Good background in linear algebra (e. , matrix and vector algebra, linear independence, basis) Basic knowledge in python programming and numpy However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms. Inner product: angles and orthogonality Inner products of functions and random variables (optional)...
Exploratory Data Analysis for Machine Learning
by Mark J Grover , Miguel Maldonado- 4.6
-
![]()
![]()
![]()
![]()
![]()
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...
Related searches
Sparse Representations in Signal and Image Processing: Fundamentals
by Michael Elad , Alona Golts- 0.0
-
![]()
![]()
![]()
![]()
![]()
5 Weeks
Learn about the field of sparse representations by understanding its fundamental theoretical and algorithmic foundations. 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. About sparse coding algorithms and their proven ability to perform well....
$149
IBM Machine Learning Professional Certificate
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning....
Unsupervised Learning with Python: Step-by-Step Tutorial!
by Packt Publishing- 2.8
-
![]()
![]()
![]()
![]()
![]()
7.5 hours on-demand video
Unsupervised learning is used for discovering the underlying structure of the data and encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. By the end of the course, you’ll apply clustering and dimensionality reduction in Machine Learning using Python as well as Master Unsupervised Learning to solve real-world problems!...
$12.99
Previous
