Search result for Types of unsupervised learning Online Courses & Certifications
FollowFilter ![]()
Filter ![]()
Showing 2,075 courses
By Category ![]()
By Subject![]()
By Language![]()
By Duration![]()
By Price![]()
Get Course Alerts by Email
Master Machine Learning with Scikit-Learn Library & Python
by Piyush Dave- 5
-
![]()
![]()
![]()
![]()
![]()
10.5 hours on-demand video
Learn Machine Learning Algorithms like Linear & Logistic Regression, SVM, KNN, KMean, NB, Decision Tree & Random Forest This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered....
$9.99
Intel® Edge AI for IoT Developers
by Stewart Christie , Michael Virgo , Soham Chatterjee , Vaidheeswaran Archana- 0.0
-
![]()
![]()
![]()
![]()
![]()
3 Months
Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications....
$399
Advanced Machine Learning and Signal Processing
by Romeo Kienzler , Nikolay Manchev- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 27 hours to complete
This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. Unsupervised Machine Learning Unsupervised Machine Learning...
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 34 hours to complete
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Unsupervised representation learning...
Introduction to Applied Machine Learning
by Anna Koop- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 7 hours to complete
By the end of the course, you will be able to clearly define a machine learning problem using two approaches. This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute. The Three Kinds of Machine Learning Unsupervised Learning Farmer Betty Tried Unsupervised Learning (required)...
Practical Machine Learning
by Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 9 hours to complete
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. Types of errors Welcome to Practical Machine Learning A Note of Explanation...
Clustering & Classification With Machine Learning In R
by Minerva Singh- 4.3
-
![]()
![]()
![]()
![]()
![]()
8 hours on-demand video
Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science! THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING: • Machine Learning, Supervised Learning, Unsupervised Learning in R...
$12.99
Related searches
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. This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute. Match Data to the needs of the learning Algorithm...
Practical Predictive Analytics: Models and Methods
by Bill Howe- 4.1
-
![]()
![]()
![]()
![]()
![]()
Approx. 7 hours to complete
Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Explain and apply a set of unsupervised learning concepts and methods Structure of a Machine Learning Problem Unsupervised Learning Introduction to Unsupervised Learning...
Scalable Machine Learning on Big Data using Apache Spark
by Romeo Kienzler- 3.8
-
![]()
![]()
![]()
![]()
![]()
Approx. 7 hours to complete
Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer. Week 4: Supervised and Unsupervised learning with SparkML...
Previous
