Machine Learning Algorithms | Machine Learning | Intellipaat

Machine Learning Algorithms | Machine Learning | Intellipaat

Learn about the most popular machine learning algorithms used in the industry for the purpose of prediction and classification.

Machine Learning is one of the most sought after courses in the modern world. An amazing details about the Machine Learning Algorithms which are used in the industry domains will be covered thorughout.

Learn Machine Learning Classification Algorithms using MATLAB

Learn Machine Learning Classification Algorithms using MATLAB

Classification is a very interesting area of machine learning (ML). Learn the basics of MATLAB and understand how to use different machine learning algorithms using MATLAB, with emphasis on the MATLAB toolbox called statistic and machine learning toolbox. Learn the common classification algorithms.

Description
This course is for you If you are being fascinated by the field of Machine Learning?

Basic Course Description

This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the esesential ideas. The following are the course outlines.

Segment 1: Instructor and Course Introduction
Segment 2: MATLAB Crash Course
Segment 3: Grabbing and Importing Dataset
Segment 4: K-Nearest Neighbor
Segment 5: Naive Bayes
Segment 6: Decision Trees
Segment 7: Discriminant Analysis
Segment 8: Support Vector Machines
Segment 9: Error Correcting Ouput Codes
Segment 10: Classification with Ensembles
Segment 11: Validation Methods
Segment 12: Evaluating Performance
At the end of this course,

You can confidently implement machine learning algorithms using MATLAB
You can perform meaningful analysis on the data
Student Testimonials!

This is the second Simpliv class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

This course is really good for a beginner. It will help you to start from ground up and move on to more complicated areas. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform. I do recommend getting yourself enrolled for this course.Excellent course and instructor. You learn all the fundamentals of using MATLAB.

Lakmal Weerasinghe

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

"Concepts are explained very well, Keep it up Sir...!!!"

Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

Your Benefits and Advantages:

You receive knowledge from a PhD. in Computer science (machine learning) with over 10 years of teaching and research experience, In addition to 15 years of programming experience and another decade of experience in using MATLAB
The instructor has 6 courses on Simpliv on MATLAB including a best seller course.
The overall rating in these courses are (4.5/5)
If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You have lifetime access to the course
You have instant and free access to any updates i add to the course
You have access to all Questions and discussions initiated by other students
You will receive my support regarding any issues related to the course
Check out the curriculum and Freely available lectures for a quick insight.

It's time to take Action!

Click the "Take This Course" button at the top right now!

Time is limited and Every second of every day is valuable.

I am excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

Who is the target audience?

Researchers, Entrepreneurs, Instructors and Teachers, College Students, Engineers, Programmers and Simulators
Basic knowledge
Just basic high level math
What will you learn
Use machines learning algorithms confidently in MALTAB
Build classification learning models and customize them based on the datasets
Compare the performance of diffferent classification algorithms
Learn the intuition behind classification algorithms
Create automatically generated reports for sharing your analysis results with friends and colleague

To continue:

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.

Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.