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

Table of contents

  1. Chapter 1 : Introduction to Course
    1. Introduction to Course
    2. Why Machine Learning
    3. Why Support Vector Machine
    4. Course Overview
  2. Chapter 2 : Introduction to Machine Learning
    1. Introduction to Machine Learning, Learning Process, and Supervised Learning
    2. Unsupervised Learning and Reinforcement Learning
    3. History and Future of Machine Learning
    4. Dataset, Label, and Features
    5. Training Data, Testing Data, and Outliers
    6. Model
    7. Model (Difference Between Classification and Regression)
    8. Model (Function, Parameters, Hyperparameters)
    9. Training a Model, Cost, Error, Loss, Risk, and Accuracy
    10. Optimization
    11. Overfitting, Underfitting, Just Right Optimum (Part 1)
    12. Overfitting, Underfitting, Just Right Optimum (Part 2)
    13. Validation and Cross Validation, Generalization, Data Snooping, Validation Set
    14. Probability Distributions and Curse of Dimensionality
    15. Small Sample Size problems, One Shot Learning
    16. Importance of Data in Machine Learning, Data Encoding, and Preprocessing
    17. General Flow of a Typical Machine Learning Project
  3. Chapter 3 : Introduction to Python
    1. Introduction to Python
    2. Introduction to IDE, Hello World
    3. Introduction to Data Type, Numbers
    4. Variable and Operators (Numbers)
    5. Variables and Operators (Rational Operators and Functions)
    6. Variables and Operators (String)
    7. Variables and Operators (String and Print Statement)
    8. Lists (Indexing, Slicing Built-In Lists in Functions)
    9. Lists (Copying a List)
    10. Tuples (Indexing, Slicing, Built-In Tuple Functions)
    11. Set (Initialize, Built-In Set Functions)
    12. Dictionary
    13. Logical Operator, Decision Making, For Loops, While Loops, Functions
    14. Logical Operator, Decision Making, For Loops, While Loops, List Comprehension
    15. Functions
    16. Calculator Project
  4. Chapter 4 : GridWorld Example
    1. Introduction to SVM
    2. Linear Discriminants
    3. Linear Discriminants higher spaces
    4. Linear Discriminants Decision Boundary
    5. Generalized Linear Model
    6. Feature Transformation
    7. Max Margin Linear Discriminant
    8. Hard Margin Versus Soft Margin
    9. Confidence
    10. Multiclass Extension
    11. SVM Versus Logistic Regression Sparsity
    12. SVM Optimization
    13. SVM Langrangian Dual
    14. Kernels
    15. Python Packages and the Titanic Dataset
    16. Using NumPy, Pandas, and Matplotlib (Part 1)
    17. Using NumPy, Pandas, and Matplotlib (Part 2)
    18. Using NumPy, Pandas, and Matplotlib (Part 3)
    19. Using NumPy, Pandas, and Matplotlib (Part 4)
    20. Using NumPy, Pandas, and Matplotlib (Part 5)
    21. Using NumPy, Pandas, and Matplotlib (Part 6)
    22. Dataset Preprocessing
    23. SVM with Sklearn
    24. SVM without Sklearn (Part 1)
    25. SVM without Sklearn (Part 2)
  5. Chapter 5 : Optional SVM Section
    1. Optional SVM Optimization (Part 1)
    2. Optional SVM Optimization (Part 2)
    3. Optional SVM Optimization (Part 3)
    4. Optional SVM Optimization (Part 4)
    5. Optional SVM Optimization (Part 5)
    6. Optional SVM Optimization (Part 6)

Product information

  • Title: Machine Learning A-Z: Support Vector Machine with Python ©
  • Author(s): AI Sciences
  • Release date: April 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801071833