Machine Learning In C++

Master Machine Learning from scratch using C++17 without built-in functions

What you'll learn
Build the machine learning algorithms using modern C++17 from scratch!
Get a deep intuition how ML works in C++ field without using Built-in methods
Use the low-level features of Modern C++11/14/17 to supercharge your algorithms
Build interesting applications using Modern C++11/14/17 and ML techniques
Optimize your algorithms with advanced performance and memory usage profiling
Program with one of the most powerful programming languages that exists today, C++
Learn how to create CMake build system
Requirements
This is NOT a C++ course so you should be Very familiar with C++ and CMake
Knowledge of Supervised and Unsupervised learning, however those will be added as new sections in the following days
Patient and motivation
Access to a computer running Windows, Mac OS X or Linux
Already installed VS code
Description
Why you should use C++Much, if not most of the software indited today is still inscribed in C++ and this has been the case for many years.Not only is C++ popular, but it is additionally a very pertinent language. If you go to GitHub you will visually perceive that there are a sizably voluminous number of active C++ repositories and C++ is additionally prodigiously active on stack overflow.There are many leading software designations inscribed entirely or partly in C++. These include the Windows, Linux, and Mac OSX operating systems!Many of the Adobe products such as Photoshop and Illustrator, the MySQL and MongoDB database engines, and many many more are written in C++.Leading tech companies use C++ for many of their products and internal research and development. These include Amazon, Apple, Microsoft, PayPal, Google, Facebook, Oracle, and many more.Can you see how building ML in C++ will open up more career opportunities for you?If more professional companies are using C++, it stands to reason that there is going to be more of a demand for C++ programmers.If it is not based web app, the ones who use Python for their ML products partly fail. However, if you are working with hardware, C++ is a must! Because C++ is a compiled language that you can easily extract the binary files which the thing machine talks. But the main reason companies should probably use C++ is because it is so powerful!C++ is super fast and is a general-purpose programming language that supports both procedure and object-oriented programming making it very flexible.It can scale easily. And it can be portable as well.C++ can do many things that other languages just can't.That's why nearly every major language has a way to interface with code written in C++.Since C++ has influenced so many languages, if you know C++ you'll likely see elements from C++ in new languages you learn.Does this course focus on algorithms, or math, or what?!?!Let's be honest - the vast majority of ML courses available online dance with the only Python language which is an interpreted one. They encourage you to use pre-build algorithms and not performance based. Although this can lead you to quick successes, in the end it will hamper your ability to understand ML structure with C++. You can only understand how to apply ML techniques if you understand the underlying algorithms.That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms and the programming language, C++. Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.A short list of what you will learn:Advanced memory profiling to enhance the performance of your algorithmsBuild apps powered by the powerful modern C++ STD librariesCreate a CMake projectDevelop programs that work either in the Windows, Linux, and Mac OSX operating systems!Write clean, easy to understand ML code with C++, no one-name variables or confusing functionsComprehend how to twist common algorithms to fit your unique use casesLearn performance-enhancing strategies that can be applied to any type of C++ codeData loading techniques, by organizing with CMake

Overview

Section 1: About the course

Lecture 1 About the course

Lecture 2 Why learn Machine learning with C++

Lecture 3 Udemy's Q&A

Lecture 4 THE MNIST DATABASE

Section 2: Introduction

Lecture 5 Important!

Lecture 6 Introduction

Lecture 7 Introduction to Data Set

Lecture 8 THE MNIST DATABASE

Section 3: ETL - Extract, Transform, Load {Theory & Coding C++}

Lecture 9 Before Starting …

Lecture 10 Creating directories

Lecture 11 Creating files

Lecture 12 Data .h header creation

Lecture 13 Data Handler .h header creation #1

Lecture 14 Data Handler .h header creation #2

Lecture 15 Data .cpp library creation

Lecture 16 Data Handler .cpp library creation #1

Lecture 17 Data Handler .cpp library creation #2

Lecture 18 Data Handler .cpp library creation #3

Lecture 19 Data Handler .cpp library creation #4

Lecture 20 Data Handler .cpp library creation #5

Lecture 21 Data Handler .cpp library creation #6

Lecture 22 The Main .cpp file creation

Lecture 23 Compiling & Bug fixing #1

Lecture 24 Executing & Bug fixing #2

Lecture 25 Debuging & Executing

Lecture 26 CMakeLists.txt file creation & Execution

Section 4: K-Nearest Neighbor (KNN) {Theory & Coding C++}

Lecture 27 Introduction

Lecture 28 Creating directories & files

Lecture 29 k-NN .h header file creation

Lecture 30 k-NN .cpp library creation #1

Lecture 31 k-NN .cpp library creation #2

Lecture 32 k-NN .cpp library creation #3

Lecture 33 k-NN .cpp library creation #4

Lecture 34 k-NN .cpp library creation #5

Lecture 35 The Main .cpp file creation & Linking in CMakeLists.txt

Lecture 36 CMake editing & Debugging & Executing

Section 5: K-Means Algorithm {Theory & Coding C++}

Lecture 37 Introduction

Lecture 38 Coheir .h file creation (Inheritance)

Lecture 39 Creation Coheir .cpp file & Inherit to KNN & Execution KNN algorithm

Lecture 40 K-Means file system creation & Linking files in CMakeLists.txt

Lecture 41 K-Means .h file creation & Struct creation

Lecture 42 K-Means .cpp file creation #1

Lecture 43 K-Means .cpp file creation #2

Lecture 44 K-Means .cpp & Debugging & CMake & Execution

Section 6: Result

Lecture 45 Performance : k-NN vs K-Means

Lecture 46 What now?

Section 7: Sources

Lecture 47 Clon from Git

Lecture 48 Unzipped Data

Lecture 49 The whole project

C++ developers interested in Machine Learning,The ones, who develops ML in Python, want to boost their career up with C++,To advance their C++ knowledge implementing in Machine Learning,Master students who work on AI