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

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76 on-demand videos & exercises
Level: All Levels
English
11hrs 10mins
Access on mobile, web and TV

Who's this course for?

This course is designed for both beginners with some programming experience or even those who know nothing about ML and SVM.
This course is for someone who is curious to learn the math behind SVM since this course contains an optional part for mathematics as well.
It is also for someone who wants to learn logistic regression from zero to hero; for someone who is an absolute beginner and has very little idea of machine learning.

What you'll learn

  • Learn the basics of machine learning.
  • Learn the basics of discriminative learning.
  • Learn the basics of linear discriminants.
  • Learn the basics of Support Vector Machine (SVM).
  • Learn the basics of the sparsity of SVM and comparison with logistic regression.
  • Learn how to implement SVM on any dataset
  • Learn the math behind SVM.

Key Features

  • Learn how to use Pandas for data analysis.
  • Learn how to use sci-kit-learn for SVM using the Titanic dataset.
  • Learn about training data, testing data, and outliers.

Course Curriculum

01

Introduction to Course

Introduction to Course
Introduction to Course
05:00
Why Machine Learning
10:21
Why Support Vector Machine
06:18
Course Overview
05:47

02

Introduction to Machine Learning

Introduction to Machine Learning
Introduction to Machine Learning, Learning Process, and Supervised Learning
17:00
Unsupervised Learning and Reinforcement Learning
08:54
History and Future of Machine Learning
14:46
Dataset, Label, and Features
15:34
Training Data, Testing Data, and Outliers
06:46
Model
07:28
Model (Difference Between Classification and Regression)
07:23
Model (Function, Parameters, Hyperparameters)
08:32
Training a Model, Cost, Error, Loss, Risk, and Accuracy
11:20
Optimization
07:46
Overfitting, Underfitting, Just Right Optimum (Part 1)
05:25
Overfitting, Underfitting, Just Right Optimum (Part 2)
02:30
Validation and Cross Validation, Generalization, Data Snooping, Validation Set
11:07
Probability Distributions and Curse of Dimensionality
08:13
Small Sample Size problems, One Shot Learning
06:06
Importance of Data in Machine Learning, Data Encoding, and Preprocessing
13:56
General Flow of a Typical Machine Learning Project
06:35

03

Introduction to Python

Introduction to Python
Introduction to Python
03:42
Introduction to IDE, Hello World
08:02
Introduction to Data Type, Numbers
06:19
Variable and Operators (Numbers)
08:11
Variables and Operators (Rational Operators and Functions)
11:49
Variables and Operators (String)
08:12
Variables and Operators (String and Print Statement)
07:30
Lists (Indexing, Slicing Built-In Lists in Functions)
21:22
Lists (Copying a List)
04:02
Tuples (Indexing, Slicing, Built-In Tuple Functions)
03:51
Set (Initialize, Built-In Set Functions)
03:56
Dictionary
04:36
Logical Operator, Decision Making, For Loops, While Loops, Functions
07:20
Logical Operator, Decision Making, For Loops, While Loops, List Comprehension
14:33
Functions
09:52
Calculator Project
18:53

04

GridWorld Example

GridWorld Example
Introduction to SVM
04:46
Linear Discriminants
08:15
Linear Discriminants higher spaces
08:22
Linear Discriminants Decision Boundary
09:35
Generalized Linear Model
10:20
Feature Transformation
11:34
Max Margin Linear Discriminant
10:19
Hard Margin Versus Soft Margin
09:11
Confidence
09:25
Multiclass Extension
13:45
SVM Versus Logistic Regression Sparsity
12:49
SVM Optimization
11:53
SVM Langrangian Dual
12:12
Kernels
07:50
Python Packages and the Titanic Dataset
07:00
Using NumPy, Pandas, and Matplotlib (Part 1)
08:10
Using NumPy, Pandas, and Matplotlib (Part 2)
06:26
Using NumPy, Pandas, and Matplotlib (Part 3)
11:35
Using NumPy, Pandas, and Matplotlib (Part 4)
13:45
Using NumPy, Pandas, and Matplotlib (Part 5)
11:29
Using NumPy, Pandas, and Matplotlib (Part 6)
10:13
Dataset Preprocessing
14:47
SVM with Sklearn
15:44
SVM without Sklearn (Part 1)
04:38
SVM without Sklearn (Part 2)
11:54

05

Optional SVM Section

Optional SVM Section
Optional SVM Optimization (Part 1)
05:28
Optional SVM Optimization (Part 2)
12:18
Optional SVM Optimization (Part 3)
12:02
Optional SVM Optimization (Part 4)
19:47
Optional SVM Optimization (Part 5)
20:56
Optional SVM Optimization (Part 6)
13:53
Certificate of Completion
Feedback on this Course
3 questions

What to know about this course

This course is truly a step by step. In every new video, we build on what has already been learned and move one extra step forward; then we assign you a small task that is solved in the beginning of the next video. 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 a human; based on that learning, your machine starts making predictions as well! We’ll be using Python as the programming language in this course, which is the hottest language nowadays when 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 the general concepts of machine learning, 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 implemented using Python. All code files and resources are placed here: https://github.com/PacktPublishing/Machine-Learning-A-Z-Support-Vector-Machine-with-Python-

About the Author

AI Sciences

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