×

Create CV Books Summaries Online Courses Blog Communities

Login

Sign up

Join Our Telegram Channel Now to Get Any New Free Courses : Click Here

Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018

Share your inquiries now with community members Click Here

Previous Lesson Next Lesson

Sign Up and Get Free Certificate

Lessons List | 20 Lesson

Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018

Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018

Lecture 2 Linear Regression and Gradient Descent | Stanford CS229 Machine Learning Autumn 2018

Lecture 2 Linear Regression and Gradient Descent | Stanford CS229 Machine Learning Autumn 2018

Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018

Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018

Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018

Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018

Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018

Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018

Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018

Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018

Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018

Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018

Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018

Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018

Lecture 9 Approx Estimation Error ERM | Stanford CS229 Machine Learning Autumn 2018

Lecture 9 Approx Estimation Error ERM | Stanford CS229 Machine Learning Autumn 2018

Lecture 10 Decision Trees and Ensemble Methods | Stanford CS229 Machine Learning Autumn 2018

Lecture 10 Decision Trees and Ensemble Methods | Stanford CS229 Machine Learning Autumn 2018

Lecture 11 Introduction to Neural Networks | Stanford CS229 Machine Learning Autumn 2018

Lecture 11 Introduction to Neural Networks | Stanford CS229 Machine Learning Autumn 2018

Lecture 12 Backprop Improving Neural Networks | Stanford CS229 Machine Learning Autumn 2018

Lecture 12 Backprop Improving Neural Networks | Stanford CS229 Machine Learning Autumn 2018

Lecture 13 Debugging ML Models and Error Analysis | Stanford CS229 Machine Learning Autumn 2018

Lecture 13 Debugging ML Models and Error Analysis | Stanford CS229 Machine Learning Autumn 2018

Lecture 14 Expectation Maximization Algorithms | Stanford CS229 Machine Learning Autumn 2018

Lecture 14 Expectation Maximization Algorithms | Stanford CS229 Machine Learning Autumn 2018

Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018

Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018

Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018

Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018

Lecture 17 MDPs Value Policy Iteration | Stanford CS229 Machine Learning Autumn 2018

Lecture 17 MDPs Value Policy Iteration | Stanford CS229 Machine Learning Autumn 2018

Lecture 18 Continous State MDP Model Simulation | Stanford CS229 Machine Learning Autumn 2018

Lecture 18 Continous State MDP Model Simulation | Stanford CS229 Machine Learning Autumn 2018

Lecture 19 Reward Model Linear Dynamical System | Stanford CS229 Machine Learning Autumn 2018

Lecture 19 Reward Model Linear Dynamical System | Stanford CS229 Machine Learning Autumn 2018

Lecture 20 RL Debugging and Diagnostics | Stanford CS229 Machine Learning Autumn 2018

Lecture 20 RL Debugging and Diagnostics | Stanford CS229 Machine Learning Autumn 2018

Comments

Our New Certified Courses Will Reach You in Our Telegram Channel
Join Our Telegram Channels to Get Best Free Courses

We Appreciate Your Feedback

Excellent		6 Reviews
Good		5 Reviews
medium		1 Reviews
Acceptable		1 Reviews
Not Good		0 Reviews

4.2

13 Reviews

FazelQader Rahmani

GOOD 2024-12-22

Roland Kpadeh

Good 2024-06-15

Akanchha

Great 2023-11-17

Abira James

Videos in this course doesn't play. It refers you to watch on YouTube. So the progress bar won't show anything. Kindly fix it ASAP. 2023-10-20

Show More Reviews

Related Courses in Computer Science

Linear Algebra for Computer Scientists

Linear Algebra for Computer Scientists

Insertion Sort

Bubble Sort

GCSE Computer Science

GCSE Computer Science

Random Access Memory

Random Access Memory

Binary Trees

Bitwise Operators

Bitwise Operators

Introduction to Stacks Data Structures

Introduction to Stacks Data Structures

Organization Theory Structure and Design

Organization Theory Structure and Design

Handling Large Scale Unit Level Data

Handling Large Scale Unit Level Data

Introduction to Machine Learning

Introduction to Machine Learning

Applied Linear Algebra Vectors Matrices

Applied Linear Algebra Vectors Matrices

Artificial Intelligence beginners

Artificial Intelligence beginners

Machine Learning Autumn 2018

Machine Learning Autumn 2018

Robotics and Autonomous Systems

Robotics and Autonomous Systems

Natural Language Understanding

Natural Language Understanding

Course Description

Machine learning Field of study Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Is machine learning hard? Why is machine learning 'hard'? ... There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.What is the goal of machine learning? Machine Learning Defined Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.What are the basics of machine learning? Key Elements of Machine Learning Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.

Trends

Recent