×
Blog Create CV Courses Certificates Online Courses Communities

Login

Sign up
MindLuster Logo
Categories
Popular

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
Sign up Now

Lessons List | 20 Lesson
Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018
01:15:20

Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018
Lecture 2 Linear Regression and Gradient Descent | Stanford CS229 Machine Learning Autumn 2018
01:18:17

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
01:19:34

Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018
Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018
01:22:02

Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018
Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018
01:18:52

Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018
Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018
01:20:57

Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018
Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018
01:20:25

Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018
Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018
01:23:26

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
01:26:03

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
01:20:41

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
01:20:14

Lecture 11 Introduction to Neural Networks | Stanford CS229 Machine Learning Autumn 2018
Lecture 12 Backprop Improving Neural Networks | Stanford CS229 Machine Learning Autumn 2018
01:16:38

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
01:18:55

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
01:20:31

Lecture 14 Expectation Maximization Algorithms | Stanford CS229 Machine Learning Autumn 2018
Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018
01:19:48

Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018
Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018
01:18:10

Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018
Lecture 17 MDPs Value Policy Iteration | Stanford CS229 Machine Learning Autumn 2018
01:19:14

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
01:20:15

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
01:21:07

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
01:12:43

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

Join Now

We Appreciate Your Feedback

Excellent
6 Reviews
Good
5 Reviews
medium
0 Reviews
Acceptable
1 Reviews
Not Good
0 Reviews
4.3
12 Reviews
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

Ram Babu.B

Nice 2023-10-06

Show More Reviews


Related Courses in Computer Science
Linear Algebra for Computer Scientists
7 Lessons

Linear Algebra for Computer Scientists

Insertion Sort
3 Lessons

Insertion Sort

Bubble Sort
4 Lessons

Bubble Sort

GCSE Computer Science
18 Lessons

GCSE Computer Science

Random Access Memory
7 Lessons

Random Access Memory

Binary Trees
4 Lessons

Binary Trees

Bitwise Operators
4 Lessons

Bitwise Operators

Introduction to Stacks Data Structures
17 Lessons

Introduction to Stacks Data Structures

Organization Theory Structure and Design
41 Lessons

Organization Theory Structure and Design

Handling Large Scale Unit Level Data Using STATA
41 Lessons

Handling Large Scale Unit Level Data Using STATA

Introduction to Machine Learning
19 Lessons

Introduction to Machine Learning

Applied Linear Algebra Vectors Matrices and Least Squares
54 Lessons

Applied Linear Algebra Vectors Matrices and Least Squares

Artificial Intelligence Principles and Techniques
1 Lessons

Artificial Intelligence Principles and Techniques

Machine Learning Autumn 2018
1 Lessons

Machine Learning Autumn 2018

Robotics and Autonomous Systems
15 Lessons

Robotics and Autonomous Systems

Natural Language Understanding
15 Lessons

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
Previous Lesson Next Lesson