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

Video description

Learn machine learning and support vector machine from scratch

About This Video

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

In Detail

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 …

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

Video description

Learn machine learning and support vector machine from scratch

About This Video

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

In Detail

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.

Publisher resources

Download Example Code

Chapter 1 : Introduction to Course

Introduction to Course

Why Machine Learning

Why Support Vector Machine

Course Overview

Chapter 2 : Introduction to Machine Learning

Introduction to Machine Learning, Learning Process, and Supervised Learning

Unsupervised Learning and Reinforcement Learning

History and Future of Machine Learning

Dataset, Label, and Features

Training Data, Testing Data, and Outliers

Model

Model (Difference Between Classification and Regression)

Model (Function, Parameters, Hyperparameters)

Training a Model, Cost, Error, Loss, Risk, and Accuracy

Optimization

Overfitting, Underfitting, Just Right Optimum (Part 1)

Overfitting, Underfitting, Just Right Optimum (Part 2)

Validation and Cross Validation, Generalization, Data Snooping, Validation Set

Probability Distributions and Curse of Dimensionality

Small Sample Size problems, One Shot Learning

Importance of Data in Machine Learning, Data Encoding, and Preprocessing

General Flow of a Typical Machine Learning Project

Chapter 3 : Introduction to Python

Introduction to Python

Introduction to IDE, Hello World

Introduction to Data Type, Numbers

Variable and Operators (Numbers)

Variables and Operators (Rational Operators and Functions)

Variables and Operators (String)

Variables and Operators (String and Print Statement)

Lists (Indexing, Slicing Built-In Lists in Functions)

Lists (Copying a List)

Tuples (Indexing, Slicing, Built-In Tuple Functions)

Set (Initialize, Built-In Set Functions)

Dictionary

Logical Operator, Decision Making, For Loops, While Loops, Functions

Logical Operator, Decision Making, For Loops, While Loops, List Comprehension

Functions

Calculator Project

Chapter 4 : GridWorld Example

Introduction to SVM

Linear Discriminants

Linear Discriminants higher spaces

Linear Discriminants Decision Boundary

Generalized Linear Model

Feature Transformation

Max Margin Linear Discriminant

Hard Margin Versus Soft Margin

Confidence

Multiclass Extension

SVM Versus Logistic Regression Sparsity

SVM Optimization

SVM Langrangian Dual

Kernels

Python Packages and the Titanic Dataset

Using NumPy, Pandas, and Matplotlib (Part 1)

Using NumPy, Pandas, and Matplotlib (Part 2)

Using NumPy, Pandas, and Matplotlib (Part 3)

Using NumPy, Pandas, and Matplotlib (Part 4)

Using NumPy, Pandas, and Matplotlib (Part 5)

Using NumPy, Pandas, and Matplotlib (Part 6)

Dataset Preprocessing

SVM with Sklearn

SVM without Sklearn (Part 1)

SVM without Sklearn (Part 2)

Chapter 5 : Optional SVM Section

Optional SVM Optimization (Part 1)

Optional SVM Optimization (Part 2)

Optional SVM Optimization (Part 3)

Optional SVM Optimization (Part 4)

Optional SVM Optimization (Part 5)

Optional SVM Optimization (Part 6)

Start your Free Trial

Self paced

Go to the Course
We have partnered with providers to bring you collection of courses, When you buy through links on our site, we may earn an affiliate commission from provider.