Code AI From The Start to The End

Code AI From the Start to The End
Published 3/2024
Duration: 6h28m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 4.32 GB

Genre: eLearning | Language: English [/center]

part of the Big Bang of Data Science

What you'll learn
Coding as an independent language; machines talk to or with
The idea of a project structure and timeline, in principle, from abstract and applied perspective
The fundamentals of development, execution, and maintenance
Principles of AI model as the backend for a software product
Framework of frontend, backend, and database in relation to AI problem solving paradigm
The guidelines of project setup, design, and execution
Comprehensive details on using Qt Designer as the tool to design desktop GUI software
Comprehensive details on using Visual Studio-VS- Code as the source-code editor
Comprehensive details on using SQLite as a database engine
Comprehensive details on using PySide6 which is a comprehensive binding of the cross-platform GUI toolkit Qt
From scratch- Project One- HCC_v01- a health care diagnostic tool using AI as its core to predict NORMAL/Pneumonia chest cancer.
From scratch- Project Two- FSP_v01- a finance managing tool using AI as its core to dragonize a stock price

Requirements
basic knowledge of research, analysis, and prediction; In addition, basic knowledge of Python as a programming language.
STRONGLY RECOMMENDED: the completion of the three books from the Big Bang of Data Science- Research, Analysis, and Prediction- from the Start to the End.

Description
This is
the fourth element
of
the Big Bang of Data Science
, that is
Code AI from the Start to The End
.
I don't want to stick to that _
abstract and direct
_ definition from the academic book, on the meaning of
coding
, but from the industrial one. So, I believe **
CODING
** is a language syntax that machines use to communicate to or with. In other words, just like humans, machines use their own language, recently in the form of a digital form of
[0, 1]
, to speak to each other or with us. So, if you
code
something it's as if you are authoring a text, where the code is the structure and syntax of that language you author of, and the rules and structure of that language must be then obeyed. As if a text is authored using a human language, the form of outcome must be a book, or a paper, or similar forms; if you
code
using any programming language, then the outcome form is going to be a
GUI
form which can be of taking input as arguments and outcome results to screen, or just a script that execute certain tasks.
So, since we have established that level of understanding on the meaning of coding, then let us connect the dots with the aim of [
The Big Bang of Data Science
]. In the main introduction of it, I have mentioned that the outcome from the first
two books
:
[Research from the start to the end]
and
[analysis from the start to the end]
you have an
analytical model
which is used as an input to the third book __
prediction from the start to the end
__ the outcome was a __
predictive model
__.
Then I set the argument that we shall use this model in somehow; that was two possible ways:
(a)
as
a backend GUI
interface, e.g. _d
esktop app, mobile app, or desktop app
_. Alternatively,
(b)
we can use the model
to embed it into a machine
_ that machine then act as smart one so to speak.
In the first edition of [
the Big Bang of Data Science
] I have established the fact
to transform this predictive model as a backend to a __GUI__ interface
. That is exactly the main aim of this material.
Where we shall discuss how to fix this __predictive model__ as a backend for an __interface__
.
We are going to use a specific form in this edition that __GUI__ or interface shall be __desktop application__
. However, this idea can span to other forms such as __mobile app__, or __web app__, but the concept is the same.
We are going to use a common programming language in this material, that is
__Python__
, however alternative options are possible as well. In addition, we shall utilize most of the
__CURD__
operations that standard operation apps would do, moreover, the utilization of
__Database__
structure, even though, the choice would be primitive, however it can span to more complicated scenarios. As you will most likely understand that Python is the language we write, so to transform that script into a GUI we can utilize available packages such as
PyQt
to accomplish that aim. Finally,
two projects
will be built,
the first
is
to solve a classification problem in health domain
, and
the second,

to watch a real-time price change from a finance domain
.
To this end, the fourth book is carefully crafted to meet all the requirements to make that kind of transformation option from __predictive model__ into a __GUI__. Here is a quick view of the content of the book.
### Introduction
1. [✓] COURSE STRATEGY
2. [✓] PROJECT & TIMELINE
3. [✓] PROGRAMMING & STRUCTURE
4. [✓] DEVELOPMENT – EXECUTION & MAINTENANCE
### Technical Setup
1. [✓] THE UNDERLYING AI PRINCIPLE
2. [✓] FRONEND & BACKEND AND DB PRINCIPLES
3. [✓] PROJECT SETUP AND ESSENTIALS
3.1. ➢ Python Language Environment
3.2. ➢ Visual Studio Code IDE Env + Virtual Env
3.3. ➢ Qt Designer App setup
3.4. ➢ Pyside6 Library setup
3.5. ➢ Sqlite3 setup
### HCC_V01_Project
1. [✓] Project Introduction
2. [✓] Environment & Setup Workflow
3. [✓] Project Execution
### FSP_V01_Project
1. [✓] Project Introduction
2. [✓] Environment & Setup Workflow
3. [✓] Project Execution
## Who is this book for?
This book is for anyone with the interest in building, creating and producing a professional product that has a future enhancement feature, in other words,
a product that is good, successful and intelligent
- in technical language it's referred to as predictive model. Aim then to utilize it using available option that is to create a
__GUI__
that user can interact with, and its backend is that predictive model.
To this end, it's
recommended to have basic knowledge about coding, research, analysis and prediction
, with extreme enthusiasm to learn how to make the right decision. So, it is meant for an audience of:
(1)
students, under or postgraduate.
(2)
scholars,
(3)
researchers,
(4)
scientists,
(5)
professionals from technical or academic background in IT, computer science or similar domain.
[!TIP]
The trainer strongly advice on learning the materials from the first book [Research from the Start to the End]; that can absolutely help you to perform way better in this book.
The trainer strongly advice on learning the materials from the second book [Analysis from the Start to the End]; that can absolutely help you to perform way better in this book.
The trainer strongly advice on learning the materials from the third book [Prediction from the Start to the End]; that can absolutely help you to perform way better in this book.
# Book competitive advantage
[!IMPORTANT]
The main title is __code__, that implies the focus on the fundamentals of programming language. Most of the material discusses the subject of coding or such within the context of computer science or similar discipline. Unlike those materials, the outlines of this book discuss the subject of coding beyond the boundaries of computer science. In fact, it introduces the idea of coding as an independent language that machines use to communicate (to or with) each other, as well as us. This principle gives you then the ability to use any programming language of your choice instead of limiting your ability within the context of one at its own.
Many materials discuss subjects of that sort of title in such a way as only coding perspective, i.e. it dives directedly into the context and the syntax of the language. Even though, could be as applied examples in form of project and implementation. __Unlike__ this approach, this material essentially shows you the story of a project or a solution you are developing from the very beginning from the stakeholder point of view. So, you learn how to ask questions, concerning the outlines of the material, then based on that you learn how formulate essential proposal about the solution that you intend to design, this includes: the design, the form of the solution, the tools you intend to use as the programming language, the database, external libraries, IDEs, etc. Moreover, it shows you how to fix the __expectation__ factor, i.e. you must be realistic on the outcome of your product, since it's not a primitive kind of software, but a predictive driven kind of solution. As a result, you may consider yourself with the title as __Solution Architect__ rather than just only _Software developer_.
One of the most important advantages of this material is the emphasis on __the state of independence__. Let me share with you an example, in the second project which we intend to build, it's a problem related to _finance_ domain. We intend to watch for a stock price change in __real time__. Since I am dealing with real-time data then the entire operation must take a different approach. Imagin the price changes in a matter of seconds, or a minute, then how we shall deal with it. You might think of reaching out directly to that price change which is operating on another network and fetching it to our local network then do whatever you want. However, this is wrong, from technical aspects you find later. Therefore, we must find sort of __middleman__ that fetch the price store it temporary and we access that storage in real-time. This is where framework as __Apache Klafka__ suggest solution of that sort. However, this material shows you how __to break free__ using simple tools within our capacity to e.g. use __FTP__ server. So, we are going to create our own Klafka feature, as fixing this FTP to fetch, store the price in __serialized way__ on temporary storage, and then we have just to access and get our work done. As a result, you will enjoy the status of being __in the state of independence__.
Finally, this material follows the approach of __simulative__ setup. In fact, I will deliver the content from the aspect as if two colleagues are working to solve a problem, rather than a training program. So, you feel as if you and I are in the same working environment to solve and produce a solution for the problem at hand.
Who this course is for:
students- post/undergraduate; scholars, scientists, professionals with IT or Natural science backgrounds

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