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Support Vector Machine A-Z: Support Vector Machine Python ©

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Support Vector Machine A-Z: Support Vector Machine Python ©, available at $69.99, has an average rating of 4.35, with 76 lectures, based on 40 reviews, and has 472 subscribers.

You will learn about Learn the basics of Machine Learning Learn basics of Discriminative Learning Learn basics of Linear Discriminants Learn basics of Support Vector Machine (SVM) Learn basics of sparsity of SVM and comparison with logistic regression Learn Data Normalization/scaling using python Learn Data Visualization using python Learn removing/replacing missing values in data using python Learn to use Pandas for Data Analysis Use SciKit-Learn for SVM using titanic data set Learn how to implement SVM on any data set Learn the maths behind SVM (Optional) This course is ideal for individuals who are This course if for someone who is curious to learn the maths behind SVM since this course also contains an optional part for mathematics as well or This course is for someone who want to learn Logistic regression from zero to hero or This course is for someone who is absolute beginner and have very little idea of machine learning It is particularly useful for This course if for someone who is curious to learn the maths behind SVM since this course also contains an optional part for mathematics as well or This course is for someone who want to learn Logistic regression from zero to hero or This course is for someone who is absolute beginner and have very little idea of machine learning.

Enroll now: Support Vector Machine A-Z: Support Vector Machine Python ©

Summary

Title: Support Vector Machine A-Z: Support Vector Machine Python ©

Price: $69.99

Average Rating: 4.35

Number of Lectures: 76

Number of Published Lectures: 76

Number of Curriculum Items: 76

Number of Published Curriculum Objects: 76

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn the basics of Machine Learning
  • Learn basics of Discriminative Learning
  • Learn basics of Linear Discriminants
  • Learn basics of Support Vector Machine (SVM)
  • Learn basics of sparsity of SVM and comparison with logistic regression
  • Learn Data Normalization/scaling using python
  • Learn Data Visualization using python
  • Learn removing/replacing missing values in data using python
  • Learn to use Pandas for Data Analysis
  • Use SciKit-Learn for SVM using titanic data set
  • Learn how to implement SVM on any data set Learn the maths behind SVM (Optional)

Who Should Attend

  • This course if for someone who is curious to learn the maths behind SVM since this course also contains an optional part for mathematics as well
  • This course is for someone who want to learn Logistic regression from zero to hero
  • This course is for someone who is absolute beginner and have very little idea of machine learning

Target Audiences

  • This course if for someone who is curious to learn the maths behind SVM since this course also contains an optional part for mathematics as well
  • This course is for someone who want to learn Logistic regression from zero to hero
  • This course is for someone who is absolute beginner and have very little idea of machine learning

Are you ready to start your path to becoming a Machine Learning expert!

Are you ready to train your machine like a father trains his son!

A breakthrough in Machine Learning would be worth ten Microsofts.” -Bill Gates

There are lots of courses and lectures out there regarding Support Vector Machine. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already been learned and move one extra step forward and then we assign you a small task that is solved at the beginning of the next video.

We start by teaching the theoretical part of the concept and then we implement everything as it is practically using python

This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like humans and based on that learning, your machine starts making predictions as well!

We’ll be using python as a programming language in this course which is the hottest language nowadays if we talk about machine learning. Python will be taught from a very basic level up to an advanced level so that any machine learning concept can be implemented.

We’ll also learn various steps of data preprocessing which allows us to make data ready for machine learning algorithms.

We’ll learn all general concepts of machine learning overall which will be followed by the implementation of one of the most important ML algorithms “Support Vector Machine”. Each and every concept of SVM will be taught theoretically and will be implemented using python.

Machine learning has been ranked one of the hottest jobs on Glassdoor and the average salary of a machine learning engineer is over $110,000 in the United States according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or even those who know nothing about ML and SVM!

This comprehensive course is comparable to other Machine Learning courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 11 hours of HD video lectures divided into 70+ small videos and detailed code notebooks for every lecture, this is one of the most comprehensive courses for Logistic regression and machine learning on Udemy!

This course is really special for you because we are teaching everything from the beginning and for those who want to go an extra mile and want to learn maths behind SVM, there is a special gift for those people as well.

We’ll teach you how to program with Python, how to use it for data preprocessing and SVM! Here are just a few of the topics that we will be learning:

Programming with Python

NumPy with Python for array handling

Using pandas Data Frames to handle Excel Files

Use matplotlib for data visualizations

Data Preprocessing

Machine Learning concepts, including:

  • Model fitting

  • Overfitting

  • Model Validation

  • Data snooping

  • Data encoding

SVM with sk-learn

SVM from absolute scratch using NumPy

Implementing SVM on different data sets

Learning mathematics behind SVM (optional)

and much, much more!

Enroll in the course and become a data scientist today!

Who this course is for:

  • This course is for you if you want to learn how to program in Python for Machine Learning

  • This course is for you if you want to make a predictive analysis model

  • This course is for you if you are tired of Machine Learning courses that are too complicated and expensive

  • This course is for you if you want to learn Python by doing

Course Curriculum

Chapter 1: Introduction to course

Lecture 1: Introduction to the Course

Lecture 2: Introduction To Instructor

Lecture 3: Why Machine Learning

Lecture 4: Why Support Vector Machine

Lecture 5: Course Overview

Lecture 6: Please give us some feedback and Review

Lecture 7: Link to the Python codes for the projects and the data

Chapter 2: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning, Learning Process and Supervised Learning

Lecture 2: UnSupervised Learning and Reinforcement Learning

Lecture 3: History and Future of Machine Learning

Lecture 4: Dataset, Label and Features

Lecture 5: Training Data,Testing Data and Outliers

Lecture 6: Model

Lecture 7: Model (Difference between Classification and Regression)

Lecture 8: Model (Function,Parameters,Hyperparameters)

Lecture 9: Training a model,Cost,Error,Loss,Risk,Accuracy

Lecture 10: Optimization

Lecture 11: Overfitting,Underfitting,Just RightOptimum (Part 1)

Lecture 12: Overfitting,Underfitting,Just RightOptimum (Part 2)

Lecture 13: Validation and Cross Validation,Generalization,Data Snooping,Validation Set

Lecture 14: Probability Distributions and Curse of Dimensionlity

Lecture 15: Small Sample Size problems,One Shot Learning

Lecture 16: Importance of Data in Machine Learning,Data Encoding and Preprocessing

Lecture 17: General Flow of a typical Machine Learning Project

Chapter 3: Introduction to Python

Lecture 1: Link to the Python codes for the projects and the data

Lecture 2: Introduction to Python

Lecture 3: Introduction to IDE,Hello World

Lecture 4: Introduction to Data Type, Numbers

Lecture 5: Variable and Operators (Numbers)

Lecture 6: Variables and Operators (Rational Operators and Functions)

Lecture 7: Variables and Operators (String)

Lecture 8: Variables and Operators (String and print Statement)

Lecture 9: Lists(Indexing,Slicing-Built in Lists Functions)

Lecture 10: Lists(Copying a List)

Lecture 11: Tuples(Indexing,Slicing,Built in Tuple Functions)

Lecture 12: Set(initialize,Built in Set Functions)

Lecture 13: Dictionary

Lecture 14: Logical Operator,Decision Making,For Loops,While Loops,Functions

Lecture 15: Logical Operator,Decision Making,For Loops,While Loops,List Comprehension

Lecture 16: Functions

Lecture 17: Calculator Project

Chapter 4: Support Vector Machine

Lecture 1: Link to the Python codes for the projects and the data

Lecture 2: Introduction SVM

Lecture 3: Linear Discriminants

Lecture 4: Linear Discriminants higher spaces

Lecture 5: Linear Discriminants Decision Boundary

Lecture 6: Generalized Linear Model

Lecture 7: Feature Transformation

Lecture 8: Max Margin Linear Discriminant

Lecture 9: Hard Margin Vs Soft Margin

Lecture 10: Confidence

Lecture 11: Multiclass Extension

Lecture 12: SVM Vs Logistic Regression Sparsity

Lecture 13: SVM Optimization

Lecture 14: SVM Langrangian Dual

Lecture 15: Kernels

Lecture 16: Python Packages & Titanic DataSet

Lecture 17: Using Numpy, Pandas and Matplotlib (Part 1)

Lecture 18: Using Numpy, Pandas and Matplotlib (Part 2)

Lecture 19: Using Numpy, Pandas and Matplotlib (Part 3)

Lecture 20: Using Numpy, Pandas and Matplotlib (Part 4)

Lecture 21: Using Numpy, Pandas and Matplotlib (Part 5)

Lecture 22: Using Numpy, Pandas and Matplotlib (Part 6)

Lecture 23: DataSet Preprocessing

Lecture 24: SVM with Sklearn

Lecture 25: SVM without Sklearn (Part 1)

Lecture 26: SVM without Sklearn (Part 2)

Chapter 5: Optional SVM Section

Lecture 1: Link to the Python codes for the projects and the data

Lecture 2: Optional SVM Optimization (Part 1)

Lecture 3: Optional SVM Optimization (Part 2)

Lecture 4: Optional SVM Optimization (Part 3)

Lecture 5: Optional SVM Optimization (Part 4)

Lecture 6: Optional SVM Optimization (Part 5)

Lecture 7: Optional SVM Optimization (Part 6)

Chapter 6: Bonus Lecture

Lecture 1: Link to the Python codes for the projects and the data

Lecture 2: THANK YOU Bonus Video

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 2 votes
  • 3 stars: 6 votes
  • 4 stars: 7 votes
  • 5 stars: 24 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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