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.
What You Will 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
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.
About The Author
AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.
Their courses have successfully helped more than 100,000 students master AI and data science.
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 (Difference Between Classification and Regression)
- Model (Function, Parameters, Hyperparameters)
- Training a Model, Cost, Error, Loss, Risk, and Accuracy
- 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)
- Logical Operator, Decision Making, For Loops, While Loops, Functions
- Logical Operator, Decision Making, For Loops, While Loops, List Comprehension
- 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
- Multiclass Extension
- SVM Versus Logistic Regression Sparsity
- SVM Optimization
- SVM Langrangian Dual
- 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
- Title: Machine Learning A-Z: Support Vector Machine with Python ©
- Release date: April 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801071833
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