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 scikitlearn 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
Table of contents
 Chapter 1 : Introduction to Course

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 BuiltIn Lists in Functions)
 Lists (Copying a List)
 Tuples (Indexing, Slicing, BuiltIn Tuple Functions)
 Set (Initialize, BuiltIn 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
Product information
 Title: Machine Learning AZ: Support Vector Machine with Python ©
 Author(s):
 Release date: April 2021
 Publisher(s): Packt Publishing
 ISBN: 9781801071833
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