Data Science: Machine Learning algorithms in Matlab
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Data Science: Machine Learning algorithms in Matlab

A-Z Guide to Implementing Classic Machine Learning Algorithms From Scratch and with Matlab and maths.
3.4 (7 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
330 students enrolled
Created by Kamal Thakur
Last updated 8/2017
English
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Current price: $10 Original price: $70 Discount: 86% off
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Includes:
  • 2.5 hours on-demand video
  • 1 Article
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Pogramming in Matlab
  • Classifier learner tool of MATLAB
  • Use Machine Learning for personal purpose
  • Make powerful analysis
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
  • Understand and implement latest researches by your own.
View Curriculum
Requirements
  • High School maths level.
  • Basic Computer programming experience
Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course will go from basics to advance. Step by step approach will make its easy to understand Machine Learning.

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don't just sit there and look at my code.


Who is the target audience?
  • Data Scientists
  • Machine Learning Algorithm Programmers
  • Neural network Researchers
  • Teachers
  • College Students
  • Entrepreneurs
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Curriculum For This Course
23 Lectures
02:31:01
+
Course Introduction
1 Lecture 02:57
+
Introduction of MATLAB
3 Lectures 06:55

Introducing MATLAB programming tool of technical computing.

Preview 02:05

Installing MATLAB / Octave.
01:31

Navigate the command line, Workspace, directory, and editor

The MATLAB Environment.
03:19

MATLAB introduction.
2 questions
+
MATLAB basic Functions
2 Lectures 14:43

Use the assignment operator to define scalar variables

MATLAB Variables : Arithmetical & Logical Operations.
08:14

Use MATLAB variables for input and output to functions. Examples include: COS, SIN, EXP, and NTHROOT.

Perform arithmetic calculations with scalars and functions using MATLAB syntax and order of operations.

MATLAB Calculations and Functions.
06:29
+
Visualization
3 Lectures 14:27
Line Plots
05:51

Annotating Graphs
03:06

Vectors and Matrices
05:30
+
Conditional statements : MATLAB programming
1 Lecture 07:12
For Loop, while loop and If-else statement.
07:12
+
Overview of Linear Algebra.
6 Lectures 42:25
Linear Algebra Theory - 1
07:29

Linear Algebra Theory - 2
07:30

Linear Algebra Theory - 3
09:40


Manipulation of DATA
05:01

Computations on DATA
06:19

Linear Algebra Quiz
2 questions
+
Introduction to Machine Learning
2 Lectures 21:42

Various types of Learning Algorithms.
08:00
+
Linear Regression Algorithm With Single Variable : Theory
3 Lectures 33:53
LR : Representation.
10:52

LR : Evaluation
09:43

LR : Optimization
13:18

Linear regression Quiz
2 questions
+
Linear Regression with single variable : Programming
2 Lectures 06:52
About Exercise
02:48

Representation : Plotting of data
04:04
About the Instructor
Kamal Thakur
3.7 Average rating
22 Reviews
2,460 Students
4 Courses
Embedded System Engineer, Instructor

Hi,

My name is Kamal thakur, I am an Electronics Engineer and electronic hobbyist with an interest in making embedded systems, Robotics understandable and enjoyable to other enthusiasts of all experience and knowledge levels. 

Industrial automation engineer with knowledge from Information Technologies to Industrial Automation.

Experienced with project design, development & commissioning, product & application technical support, training & consulting services with international environment.

Always eager to learn, I invested a lot of time in learning and teaching, covering a wide range of different scientific topics. Being an electronics engineer, Today I am passionate about data science, artificial intelligence and deep learning for Robotics. I will do my very best to convey my passion for data science to you. I have gained diverse experience in this field. I spent one year doing research in machine learning, working on innovative and exciting projects.

Looking forward to working together!