If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
How to pick a machine learning model 3: Choosing a loss function
Part of the End-to-End Machine Learning School course library at http://e2eml.school See these concepts used in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Watch the rest of the How to Choose a Model serie
From playlist E2EML 171. How to Choose Model
In this video, you’ll learn more about the different types of learning styles, to see which one works best for you! Visit https://www.gcflearnfree.org/ to learn even more. We hope you enjoy!
From playlist Fundamentals of Learning
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit https://www.gcflearnfree.org/thenow/what-is-machine-learning/1/ for our text-based lesson. This video includes information on: • How machine learning works • How machine learning i
From playlist Machine Learning
Machine Learning by Andrew Ng [Coursera] 06-02 Machine learning system design
From playlist Machine Learning by Professor Andrew Ng
I use this clip to illustrate the danger of assumptions, especially when using a formula derived by someone else with approximations!
From playlist Clips for Class
Introduction (4): Complexity and Overfitting
Simple vs complex models; training vs testing error; overfitting
From playlist cs273a
Machine Learning: Zero to Hero
This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you
From playlist Machine Learning
Lecture 06-01 Advice for applying machine learning
Machine Learning by Andrew Ng [Coursera] 0601 Deciding what to try next 0602 Evaluating a hypothesis 0603 Model selection and training/validation/test sets 0604 Diagnosing bias vs variance 0605 Regularization and bias/variance 0606 Learning curves 0607 Deciding what to try next (revisited
From playlist Machine Learning by Professor Andrew Ng
Lecture 3/16 : The backpropagation learning procedure
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 3A Learning the weights of a linear neuron 3B The error surface for a linear neuron 3C Learning the weights of a logistic output neuron 3D The backpropagation algorithm 3E How to use the derivatives computed by the ba
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course
From playlist Machine Learning Course - CS 156
Cost Function In Machine Learning With Example | Machine Learning Tutorial | Simplilearn
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From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]
Lecture 9 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. This course provides a broad introduction
From playlist Lecture Collection | Machine Learning
Bias-Variance In Machine Learning | Bias Variance Trade Off | Machine Learning Training | Edureka
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training ** This Edureka video on 'Bias Variance In Machine Learning' covers the concept of bias and variance in a machine learning model and how it affects the performance of the model. Foll
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Machine Learning by Andrew Ng [Coursera] 06-01 Advice for applying machine learning
From playlist Machine Learning by Professor Andrew Ng
Deep Learning Lecture 2.4 - Statistical Estimator Theory
Deep Learning Lecture - Estimator Theory 3: - Statistical Estimator Theory - Bias, Variance and Noise - Results for Linear Least Square Regression
From playlist Deep Learning Lecture
Machine learning describes computer systems that are able to automatically perform tasks based on data. A machine learning system takes data as input and produces an approach or solution to a task as output, without the need for human intervention. Machine learning is closely tied to th
From playlist Data Science Dictionary
Bias & Variance In Machine Learning | Bias Variance Tradeoff |Machine Learning Tutorial |Simplilearn
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From playlist AI & Machine Learning | Ronald Van Loon [2022 Updated]