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The Big Bang of Data Science- Prediction from the Start to The End- [Book Three]
https://github.com/dahmansphi/prediction_from_start_to_end

artificial-intelligence-algorithms classification clustering-algorithm cnn-keras machine-learning-algorithms predictive-modeling probability-modeling probability-prediction python reinforcement-learning rnn-tensorflow

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The Big Bang of Data Science- Prediction from the Start to The End- [Book Three]

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README 

          
> [!IMPORTANT] 

> make sure to check the book [competitive advantages](#book-competitive-advantage) below. 



![the big bang of data science banner.](/assets/cover_page.jpg)



> [!NOTE]

> 1. To view the *project video introduction* please visit [Main Introduction to The Big Bang of Data Science- First Edition](https://youtu.be/0weCBnNO7tk).

> 2. To view the *project documentation* please visit [The Big Bang of Data Science Project](https://github.com/dahmansphi/big_bang_of_data_science_project)



# About the Third Book- Prediction From the Start to the End



> [!IMPORTANT]

> 1. you can have an author introduction to this book on [Welcome, by the author, to the Third Book- Prediction from the Start to The End](https://youtu.be/yERHOW5UfvU) 

> 2. You can have a video presentation to this book on [Prediction from The Start to The End- Chapters Review](https://youtu.be/g84VMCxVoPs)

> 3. you can have a video screencast on the all the chapters' contents on [Screencasts from Prediction from the Start to The End Chapters- Book Three](https://youtu.be/mkkH47ZImbw)

> 4. you can access the code of LAB-[Part-ONE](https://github.com/dahmansphi/prediction_from_start_to_end/blob/main/lab/part_one/book_3_LAB_code_part_one.ipynb)

> 5. you can access the code of LAB-[Part-TWO](https://github.com/dahmansphi/prediction_from_start_to_end/blob/main/lab/part_two/book_3_LAB_code_part_two.ipynb)



## Author's Words

Welcome to **Prediction From the Start to the End** official documentation, the **third book** from [The Big Bang of Datat Science](https://github.com/dahmansphi/big_bang_of_data_science_project). I am Dr. Deniz Dahman the creator of the [BireyselValue algorithm](https://github.com/dahmansphi/bireyselvalue_v1/tree/main) and the author of this digital book. In the following section you will have a brief introduction on the main contents of this book.  

In addition, a reference to available [outlets](#outlets) where you may have access to the entire recorded lessons. Before going ahead, I would like to let you know that I have done this project as an independent scientist without any fund or similar capacity. I am dedicated to proceeding and seek further improvement of the content of this material. To this end if you wish to contribute in any way to this work, please find further details in the contributing section.  

  

## Contributing 



If you wish to contribute to the creator of this method and the author, you may want to check possible ways on: 



> `To Contribute in any way possible, thank you, you can check` :



1. view options to subscribe on [Dahman's Phi Services Website](https://dahmansphi.com/subscriptions/)

2. subscribe to this channel [Dahman's Phi Services](https://www.youtube.com/@dahmansphi)     

3. you can support on [patreon](https://patreon.com/user?u=118924481) 



If you prefer *any other way of contribution*, please feel free to contact me directly on [contact](https://dahmansphi.com/contact/). 



*Thank you*



# Book THREE- Prediction From the start to the end



## Book Cover

![the prediction cover book.](/assets/titles_prediction_s2e.png)

> [!TIP]

> You can have full access to the material of this book from the [outlet](#outlets) section below. Thank you.



## The Content



This is the third element of **the Big Bang of Data Science**, **`Prediction 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 prediction, but from the industrial one. So, I believe **PREDICTION** is the _co-concertmaster_ that sits in the third chair of the highest leadership position among all the other parts that are responsible for the outcome of a product that is good, successful, and intelligent. 



The first two elements from **the Big Bang of Data Science**, these are _Research from the Start to the End_ and _Analysis from the start to the End_; were responsible to outcome a product that is **GOOD, SUCCESSFUL**, of course with main characteristic of being a quality one, the technical name of this product is known as _analytical model_. However, we knew back then that product leaks an important characteristic that to be **INTELLIGENT**. We have discussed the fact that to enhance that feature of intelligence into the equation of your product, then you must factor in the **FUTURE**.  



The **PREDICTION** block is responsible for that kind of enhancement. Essentially, this black box comprises specific elements that shape the outcome of the _analytical model_ into a _predictive model_. In other words, the prediction step enhances the future element into the equation of the analytical model.



There are many tools, frameworks, and similar elements of that kind offer variant types of black boxes to do that kind of enhancement; using any of which are of no harm, however, the only disadvantage is the state of **dependency**. Let me share with you a story I call it __the text editor and you__. Let us assume you have a task to _edit_ a text which you have _authored_. What options are available to accomplish such a task? Obviously, you would employ _text editing tool_ to help you with such task. Let us assume that you have come across specific feature in that editing process and you found that your text editor does not offer such feature. Most probably, you would try to find another tool that has that feature; however, the dilemma arose when you find out that there are no such tool has such feature. Then what would you do? I believe if you have no options, then you would stick within the boundaries of that tool.  



That is the same choice you would end up with, if you rely on those black boxes of AI models without comprehensive understanding of their elements. To this end, __if you are seeking a complete state of independence__ being able to __customize, create, or reengineer__ such AI models then you are at the right place. 



This book material offers you a comprehensive understanding from __abstract and applied__ perspective on every window to become __AI Models Creator__. Firstly, you will have a clear understanding about mathematics inform of disciplines and as an independent language. Secondly, you will learn about selected outlines from math which you need to learn to make your own predictive model from scratch. Finally, you will enjoy the __map of prediction__ which shows you every possible kind of predictive model and above all you don't only learn the abstract but also the applied using Python.



To this end, **the third book** is carefully crafted to meet all the requirements to build your product on the right foundation of prediction. Here is a quick view of the content of the book. 



### Introduction 



1. [✓] Course Strategy   

2. [✓] Principles of data  

3. [✓] Data Platform  

4. [✓] Timeline representative 



### The Story of Math 



1. [✓] Philosophy of math  

2. [✓] Area of mathematics



2.1. ➢ Geometry



2.2. ➢ Algebra



2.3. ➢ Calculus & Analysis



2.4. ➢ Discrete Mathematics



2.5. ➢ Math Logic



2.6. ➢ Decision Science 



2.7. ➢ Computational Math



### You must know 



1. [✓] Number Properties

2. [✓] The universe of polynomial

3. [✓] Equation & Function & System 

4. [✓] Trigonometry 

5. [✓] e & Natural Logarithm ln

6. [✓] Exponential Function & Logarithm

7. [✓] Derivatives & Integrals 

8. [✓] Matrix, Eigenvalue, Eigenvector 

9. [✓] combination and permutation

10. [✓] Python LAB- implementation on the abstract outlines 



### Wourld of Prediction



1. [✓] Introduction to Prediction

2. [✓] Map of prediction 

3. [✓] Elaboration on the map from left 

4. [✓] Elaboration on the map from right 

5. [✓] Elaboration on the map from source  



### Prediction by Probability 



1. [✓] Introduction to Probability

2. [✓] Univariate concept of probability- Discrete value

3. [✓] Univariate concept of probability- Continues value

4. [✓] Bivariate concept of probability

5. [✓] Multivariate concept of probability

6. [✓] Probability predictive model-discrete value using: Bernouil; Binomial; Geometric; Pascal; & Hypermetric distribution

7. [✓] Probability predictive model-continues value using: uniform; exponential; normal; gamma; and cauchy distribution

8. [✓] Python Lab- implementation on every probability model



### Prediction by RCC 



1. [✓] Introduction to RCC

2. [✓] Introdution to Regression



2.1. ➢ Simple Linear Regression



2.2. ➢ Multiple Linear Regression



2.3. ➢ Nonlinear Regression



2.4. ➢ Regulization- LASO & RIDGE



3. [✓] Introduction to Classification



3.1. ➢ Logistic Regression



3.2. ➢ K-nearst Neighbor



3.3. ➢ Decision Tree



3.4. ➢ Support Vector Machine



3.5. ➢ Naive Bayse



3.6. ➢ Deep Learning- Introduction



3.7. ➢ Deep Learning- Neural Network Framework



3.8. ➢ Deep Learning- RCC Framework



3.9. ➢ Deep Learning- Reinforcement Learning Framework



3.10. ➢ Deep Learning- RNN Framework



4. [✓] Introduction to Clustering

5. [✓] Feature Engineering intro



5.1. ➢ Data Preparation



5.2. ➢ Feature Reduction



### APPENDIX- Python from the start to the End

1. [✓] Introduction

2. [✓] Setup environment 

3. [✓] Informal introduction to python 

4. [✓] Control flow tool kit

5. [✓] Data structure 

6. [✓] Modules

7. [✓] Input & output 

8. [✓] Errors & exceptions 

9. [✓] Classes 

10. [✓] Tour of the standard libraries 

11. [✓] Development tips

12. [✓] NumPy package

13. [✓] Pandas package

14. [✓] Tensor Flow basics



## 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. it's recommended to have basic knowledg about elementry math, research and analysis, 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](https://github.com/dahmansphi/research_from_start_to_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](https://github.com/dahmansphi/analysis_from_start_to_end); that can absolutely help you to perform way better in this book.



# Book competitive advantage



> [!IMPORTANT] 

> 1. The main principle of this material is to fix you in the state of independence, where you can build, assemble and create your complete own AI model. For that reason, the main element to accomplish that is comprehensive level of understanding Math, so the first step it shows you how math is an independent language, and then introduces you the main field of math for you to conceptualize its importance.. 

> 2. Based on the first advantage, the material selects several subjects of math that you must know and master. Those subjects are discussed not from a rigid abstract of math but from a wider level of understanding the use of it as an independent language. Moreover, every line of this subject is applied with real life example and code implementation in Python, in addition to visualization ability.

> 3. The map of prediction is a very unique way this material illustrates the entire world of AI. It shows you the input process and output of this phenomenon. More important, in every possible type of predictive model, e.g. regression, classification or clustering, it implements them using several common types of algorithms and methods from scratch to the end. In this way you will have strong ability from abstract and applied way.

> 4. Unlike many materials that speak about the subject of AI and machine learning, it presents some common frameworks as the basis, this material shows you that you can utilize any of those frameworks, or even re-engineer them your way. For instance, it shows you frameworks of Neural network, CNN, RNN, Reinforcement learning, which are discussed as an independent training programs, but once you master the math of AI you will see that you only need few minutes to build from scratch on your own. Moreover, you will write your own code to do so using Python.

> 5. Finally, if you have come from background with no experience in code writing, this material introduces a whole appendix coaching you on learning Python from the start to the end. so, in this case, you can even procced from the very beginning.     



# Outlets



you can have access to the recorded lessons of this book from these outlets:

1. [outlet_1 + Discount](https://dahmansphi.com/prediction-s2e/). 

2. [outlet_2](https://www.udemy.com/course/prediction-s2e/?referralCode=EC40080CDAEB20167DFA)



# The digital copy 

you can have access to the ppt digital copy in pdf format from [digital ppt book](/assets/prediction_s2e_final.pdf)