wiki

Conversation Code III: mudanças entre as edições

Criou página com '== Goals == This training aims to develop core AI knowledge by leveraging Python's toolset, focusing on conversational English and using an adapted Agile methodology called '...'
 
 
(12 revisões intermediárias pelo mesmo usuário não estão sendo mostradas)
Linha 1: Linha 1:
== Goals ==
== Goals ==


This training aims to develop core AI knowledge by leveraging Python's toolset, focusing on conversational English and using an adapted Agile methodology called ''JACI - Just, Autodidact, Collaborative and Intense''. We'll be studying Python first, going through the basics and the necessary toolset in order to prepare for the AI course.
The aim of this training is to introduce participants to the Linux operating system, in this case Ubuntu 20.04, and to enable them to use the terminal quickly and intuitively, as well as to identify errors and find solutions. In addition, we'll talk briefly about github and libraries that are widely used in programming.


== Planning ==
====Final Project====
<br>
* Sprint Planning - Presentation [https://algarnet-my.sharepoint.com/:p:/r/personal/lclaudio_inovacaobrain_com_br/Documents/Brain/BIRD%202024/Planos%20Bird/Cultura%20Bird/Treinamentos/Conversation%20Code%20II/Planning%20Conversation%20Code%20II.pptx?d=w9b762a3eee0e4f5eb958e438b08aaad2&csf=1&web=1&e=4z6NIL]
* Start day: 30/10/2023
* End day 10/03/2024
* Hours of dedication: 2hours a day
* Task Repository [https://drive.google.com/drive/folders/156-BML4bVgLqg9drPYg_QFT97B3XYbxk]
 
== Agile Methodology==
:: We will modify the Agile methodology to enhance the management of our training. In this scenario, we will incorporate sprints to determine the specific topics we will be studying over a two-week period. Alongside the sprints, we will conduct Daily meetings  once a week to facilitate efficient communication among team members, ensure progress updates, identify potential obstacles, and foster collaboration. Along this we still have a Sprint Planning that is a meeting with all the members that defines which chapter will fit in which sprint. At the end of each sprint, we will do a review for evaluations and revisions of the work sequence.
<br>


*Scrum:
By the end of this course, the participants will be able to:
**Scrum Master: [[Clara Conhalato Simão]]
*Install Ubuntu 20.04 in a virtual machine
** Sprints: two-week period
*Setup all the necessary libraries and extentions to use Ubuntu as needed
** Daily: once a week
*Install and configure a Ubuntu as a dual boot in a real machine
** Sprint Planning: defines which chapter will fit in which sprint
** Python course calendar
[[Arquivo:schedulePythonCCII.png]]
** IA course calendar
[[Arquivo:scheduleMLCCII.png]]
<br>


== J.A.C.I. Method ==
==J.A.C.I. Method==
*Meanig:
*Meanig:
** '''Just:''' Adapts to the level of each participant.
'''J'''ust: Adapts to the level of each participant.
** '''Autodidact:''' Material for reading and use the chat group to debate
** '''Collaborative:''' The main goal of this project is to help each other learn more about programming by sharing your knowledge.
** '''Intense:''' Since day one the students have exercises and a final project.
<br>


== Material ==
'''A'''utodidact: Material for reading and use the chat group to debate


*First Sprint
'''C'''ollaborative: The main goal of this project is to help each other learn more about programming by sharing your knowledge.
**NumPy: análise numérica eficiente com Python |Alura[https://cursos.alura.com.br/course/numpy-analise-numerica-eficiente-pythons]
**Curso de Python para Análise de Dados - Do Zero ao Avançado | Youtube | Aulas 09-01 até 10-01 [https://www.youtube.com/playlist?list=PLfrA8M5OrNaL9ojkFJhE6xKznrVwqYf5n]
<br>


**Exercises:  
'''I'''ntense: Since day one the students have exercises and a final project.
#Get the dimensions of the following data (both links direct to the same archive) [https://raw.githubusercontent.com/allanspadini/numpy/dados/citrus.csv] [https://algarnet-my.sharepoint.com/:x:/r/personal/lclaudio_inovacaobrain_com_br/Documents/Brain/BIRD%202024/Planos%20Bird/Cultura%20Bird/Treinamentos/Conversation%20Code%20II/citrus.csv?d=w830bcccea35d4061bde07225285747b3&csf=1&web=1&e=UPKt1k]
#Using the previousdata_citrus dataset, for both oranges and grapefruits, create an array for their weight (column 0) and another one for their diameter (column 1). Oranges range up to row 4999 and grapefruits start at row 5000.
#For each of the previous oranges_weight and grapefruits_weight arrays, calculate the equation for a line that best fits each one and plot the line.Use: [[Arquivo:CCII.png]]
#For each of the previous oranges_weight and grapefruits_weight arrays, find the slope using random numbers. Assume that b = 100.


== Planning ==
<br>
<br>
<br>
===Lesson 0 - Prerequisites:===
*Install Linux (Ubuntu 20.04) on a virtual machine // https://www.youtube.com/watch?v=Vl6f8_vin9M
*Create a GitHub account // https://docs.github.com/pt/get-started/start-your-journey/creating-an-account-on-github


----
===Lesson 1 - Introduction to Linux===
*Present the system,
*Advantages and disadvantages,
*Why use Ubuntu 20.04 and not later versions?
*Explaining the teaching method
*(Here I will give a presentation for the first class)


*Second Sprint
===Lesson 2 - Using the Terminal===
**Curso Online Pandas: conhecendo a biblioteca | Alura [https://cursos.alura.com.br/course/pandas-conhecendo-biblioteca]
*"cd/", "cd.." commands
**Pandas I/O: trabalhando com diferentes formatos de arquivos| Alura [https://cursos.alura.com.br/course/pandas-io-trabalhando-diferentes-formatos-arquivos]
*Creating Folders
**Curso de Python para Análise de Dados - Do Zero ao Avançado | Youtube | Aulas 11-01 e 11-02 [https://www.youtube.com/playlist?list=PLfrA8M5OrNaL9ojkFJhE6xKznrVwqYf5n]
*Creating files
**Curso de Pandas - Python (Aula 0.1) - Abrindo arquivos em CSV | Youtube [https://www.youtube.com/watch?v=K1RLuCp_LvI]
*Creating Folders and Files within existing folders
**Curso de Pandas - Python (Aula 0.2) - Abrindo arquivos de Excel | Youtube [https://www.youtube.com/watch?v=1HhyfrcM_9k]
*Deleting files and folders
**Convertendo arquivos JSON para Dataframes Pandas - 03JAN2023| Youtube [https://www.youtube.com/watch?v=xkTO69DjItM]
*Understanding the "path" of a file
**Raspagem de dados de tabelas HTML com Pandas | Dica de Pandas #13 | Youtube [https://www.youtube.com/watch?v=0YX0_GZeQwU]
*"Sudo", "pip"..
<br>
*Link to the supporting material - (https://ubuntu.com/tutorials/command-line-for-beginners#1-overview and https://www.youtube.com/playlist?list=PLZOToVAK85Mog5j1WL5u9zKHig-n50haG)


*Exercises -Getting started with Pandas:
====(Mini-Project L2)====
<br>
*Step 1)) Create a main Folder called "Conversation Code", inside this folder create a second folder named "Ubuntu_20_04" and inside this second folder create the files "Lesson_2.txt", "Lesson_2_to_delete" and "Lesson_2_rename".
#Import the following file[https://raw.githubusercontent.com/alura-cursos/pandas-conhecendo-a-biblioteca/main/desafios/alunos.xlsx]
*Step 2)) Delete the folder named "to delete", and rename the folder "Rename" to "Lesson_2_+Your_name+birthdate".
##Store the file's content in a Pandas DataFrame.
*Step 3)) Obtain the .zip file provided in the course, extract it to your computer and:
##View the DataFrame's first 7 rows and the last 5 rows.
**Find all the folders present in the .zip
##Check the row and column count of the DataFrame.
**Find the names of the files present in the "images" folder
##Explore the columns and analyze the data type of each one.
**Open the image "brain"
##Extra: calculate some basic statistics for the data (mean, standard deviation, etc). Tip: search for the describe method.
**Insert your created folders into the "insert_here" folder extracted from the .zip
*Step 4)) Zip your new folder with all the documents from steps 1 to 3, collect all the commands used (screenshots), and upload them to your personal GitHub.


#Using apartments.csv:
===Lesson 3 - Main Installations===
##Calculate the average of bedrooms per apartment
*Install Python 3
##How many neighborhoods are there in the dataset?
*Install C++/C#
##Which neighborhoods have the highest average rent prices?
*Install JS
##Create a horizontal bar plot presenting the 5 neighborhoods with the highest average rent prices.
*Install "git" commands
*Install Jupyter
*Confirm installation of programs/installed version


#Using this dataset, do the following [[Arquivo:apartaments.xlsx]] [https://algarnet-my.sharepoint.com/:x:/r/personal/lclaudio_inovacaobrain_com_br/Documents/Brain/BIRD%202024/Planos%20Bird/Cultura%20Bird/Treinamentos/Conversation%20Code%20II/Apartaments.xlsx?d=w5ed73e74141b4a3f823155e61c39c1cb&csf=1&web=1&e=yzvJPD]
====(Mini-Project L3)====
##Check the dataset for null values, and if there any, clean the data as you wish.
*Verify the version of all installations and check if updates are available
##Students "Alice" and "Carlos" are not part of the class anymore. Remove them from the dataset.
*Create a folder inside the "Ubuntu_20_04" folder named "Lesson_3". Inside this folder, create one file for each type .py, .cpp, .js, etc.
##Apply a filter to select only those who were approved.
*For each file created in the previous step, print "Hello world" to be used in Lesson 4.
##Save the DataFrame containing only those approved in a CSV file called approved_students.csv.
*Upload the screenshots of the commands and the .zip of the folders from this lesson to your GitHub.
##Extra: Students who scored 7.0, in fact, had an extra point that wasn't accounted for. Replace all 7.0 scores for an 8.0 score. Tip: search for the replace method.


#Using this dataset, do the following (both links direct to the same archive) [https://raw.githubusercontent.com/alura-cursos/pandas-conhecendo-a-biblioteca/main/desafios/alunos.csv] [https://algarnet-my.sharepoint.com/:x:/r/personal/lclaudio_inovacaobrain_com_br/Documents/Brain/BIRD%202024/Planos%20Bird/Cultura%20Bird/Treinamentos/Conversation%20Code%20II/alunos.csv?d=w09e1a4cd37f84716a59320d1f95b8029&csf=1&web=1&e=VleFBe]
===Lesson 4 - Main Commands===
##The students did an extracurricular activity and won extra points. These extra points are equal to 40% of the current student score. Create a column named
*"source.devel/setup.bash"
##Extra_points" containing the extra points of each student, that is, 40% of their current score.
*How to clone a repository from GitHub
##Create one more column named "Final_score" containing the score of each student added to their extra points.
*How to run a Python file... (the Hello world files created in the previous project)
##As there were extra points given, some students that weren't approved earlier may have been approved now. Based on that, create a column called "Now_approved" with the following values:
*Identifying errors when running programs
##True: if the student was approved (final score must be greater than or equal to 6)
*Commands "list", "locate", "whereis" and others. (https://www.youtube.com/watch?v=8hnxsLrJxCg , https://www.youtube.com/watch?v=PPztOjy6cLU)  
##False: if the student failed the class (final score must be lower than 6)
##Select students that weren't approved earlier but made it after the extra points


== Team==
====(Mini-Project L4) ====
# [[Clara Conhalato Simão]]
*Step 1)) Create the folder "Lesson_4" inside the "Ubuntu_20_04" folder and copy repository X from GitHub Y. Remember to check if the files are executable.
# [[Marcus Vinícius Torres Silva]]
*Step 2)) Go to the "Codes" folder and request the list of files from the copied repository.
# [[Ingrid Lima Cro Rossi]]
*Step 3)) Run the list of files present in this Folder.
# [[Gabriel Carneiro Marques Amado]]
*Step 4)) Print the results and upload them along with the complete folder to your GitHub.
# [[Amanda Varnier Massarioli de Oliveira]]
#[[Guilherme Almeida Andrade]]
# [[Gessyca Carneiro Bernardes]]
# Vinícius Araujo
# Ana Lídia Costa Nunes
#Lucas Farias Nogueira
#Gustavo Almeida Santos
#Pedro Afonso Silva
# Vitor Hugo Vasconcelos de Melo
#Pedro Henrique Afonso
# Eduardo Rosa Afonso
#Pedro Henrique Bohling Peres
# Thiago Eichenberger
#Gabriella Sousa Queiroz
#Rafael Gil Nascimento


<br>
===Lesson 5 - Adapting My Library===
 
*Install necessary programs to run files
=Python=
*Installation of: "Pandas", "TensorFlow", "PyTorch", "Numpy"
<br>
*"matplotlib", "Scikit-learn", "Keras"...
Studying Python as a programming language is often recommended for various reasons. Python is known for its simple and readable syntax, making it accessible to beginners and reducing the learning curve. Additionally, it is a versatile language used in various applications such as web development, data analysis, machine learning, and automation. It is also cross-platform and in high demand in the job market, opening doors to various opportunities.
*Copy the X2 Folder from GitHub Y2 to the "Lesson 5" folder in the "Ubuntu_20_04" folder.
 
The Python developer community is known for being welcoming and friendly, facilitating collaboration on open-source projects. There is an abundance of learning resources available, including free tutorials, online courses, and books. Moreover, Python is well-suited for rapid prototyping, allowing for the quick testing of concepts and the development of solutions with agility. However, the choice of a programming language should always be guided by the specific goals of each individual's project or programming career.
<br>
 
 
= AI with Data Science =
<br>
 
:: Data science uses AI (and its subset, Machine Learning) to interpret historical data, recognize patterns, and make predictions. In this case, AI and Machine Learning assist data scientists in gathering data in the form of insights.
<br>


:: As mentioned, Machine Learning is a branch of AI, advancing data science to the next level of automation. Machine Learning algorithms are trained on data provided by data science to become smarter and more informed when making predictions. Therefore, Machine Learning algorithms rely on data, as they won't learn without using them as a training set.
====(Mini-Project L5) ====
*Try to run more complex programs with small errors that can be identified using the terminal (possibly the need to install an extension, or indentation error..., simple things to discover via the terminal)
*Upload the codes with corrections as well as the screenshots of the errors found and how you arrived at the proposed solution.


===Lesson 6 - Enhancing your Linux Experience ===
*Creating a shortcut in Linux
*How to navegate a file content and edit it through the terminal
*How to unzip and untar files from the terminal
*Updating GIT projects without cloning again


== Team in charge ==  
== Team in charge ==  


<ol>
<ol>
<li>[[Gessyca Carneiro Bernardes]](AI instructor)</li>
<li>[[Lucas Gomes]](Linux instructor)</li>
<li>[[Gabriel Carneiro Marques Amado]] (Python instructor)</li>
<li>[[Luigi Negrini]](Inscripton Manager)</li>
<li>[[Clara Conhalato Simão]] (English instructor)</li>
</ol>
</ol>

Edição atual tal como às 18h35min de 17 de junho de 2024

Goals

The aim of this training is to introduce participants to the Linux operating system, in this case Ubuntu 20.04, and to enable them to use the terminal quickly and intuitively, as well as to identify errors and find solutions. In addition, we'll talk briefly about github and libraries that are widely used in programming.

Final Project

By the end of this course, the participants will be able to:

  • Install Ubuntu 20.04 in a virtual machine
  • Setup all the necessary libraries and extentions to use Ubuntu as needed
  • Install and configure a Ubuntu as a dual boot in a real machine

J.A.C.I. Method

  • Meanig:

Just: Adapts to the level of each participant.

Autodidact: Material for reading and use the chat group to debate

Collaborative: The main goal of this project is to help each other learn more about programming by sharing your knowledge.

Intense: Since day one the students have exercises and a final project.

Planning


Lesson 0 - Prerequisites:

Lesson 1 - Introduction to Linux

  • Present the system,
  • Advantages and disadvantages,
  • Why use Ubuntu 20.04 and not later versions?
  • Explaining the teaching method
  • (Here I will give a presentation for the first class)

Lesson 2 - Using the Terminal

(Mini-Project L2)

  • Step 1)) Create a main Folder called "Conversation Code", inside this folder create a second folder named "Ubuntu_20_04" and inside this second folder create the files "Lesson_2.txt", "Lesson_2_to_delete" and "Lesson_2_rename".
  • Step 2)) Delete the folder named "to delete", and rename the folder "Rename" to "Lesson_2_+Your_name+birthdate".
  • Step 3)) Obtain the .zip file provided in the course, extract it to your computer and:
    • Find all the folders present in the .zip
    • Find the names of the files present in the "images" folder
    • Open the image "brain"
    • Insert your created folders into the "insert_here" folder extracted from the .zip
  • Step 4)) Zip your new folder with all the documents from steps 1 to 3, collect all the commands used (screenshots), and upload them to your personal GitHub.

Lesson 3 - Main Installations

  • Install Python 3
  • Install C++/C#
  • Install JS
  • Install "git" commands
  • Install Jupyter
  • Confirm installation of programs/installed version

(Mini-Project L3)

  • Verify the version of all installations and check if updates are available
  • Create a folder inside the "Ubuntu_20_04" folder named "Lesson_3". Inside this folder, create one file for each type .py, .cpp, .js, etc.
  • For each file created in the previous step, print "Hello world" to be used in Lesson 4.
  • Upload the screenshots of the commands and the .zip of the folders from this lesson to your GitHub.

Lesson 4 - Main Commands

(Mini-Project L4)

  • Step 1)) Create the folder "Lesson_4" inside the "Ubuntu_20_04" folder and copy repository X from GitHub Y. Remember to check if the files are executable.
  • Step 2)) Go to the "Codes" folder and request the list of files from the copied repository.
  • Step 3)) Run the list of files present in this Folder.
  • Step 4)) Print the results and upload them along with the complete folder to your GitHub.

Lesson 5 - Adapting My Library

  • Install necessary programs to run files
  • Installation of: "Pandas", "TensorFlow", "PyTorch", "Numpy"
  • "matplotlib", "Scikit-learn", "Keras"...
  • Copy the X2 Folder from GitHub Y2 to the "Lesson 5" folder in the "Ubuntu_20_04" folder.

(Mini-Project L5)

  • Try to run more complex programs with small errors that can be identified using the terminal (possibly the need to install an extension, or indentation error..., simple things to discover via the terminal)
  • Upload the codes with corrections as well as the screenshots of the errors found and how you arrived at the proposed solution.

Lesson 6 - Enhancing your Linux Experience

  • Creating a shortcut in Linux
  • How to navegate a file content and edit it through the terminal
  • How to unzip and untar files from the terminal
  • Updating GIT projects without cloning again

Team in charge

  1. Lucas Gomes(Linux instructor)
  2. Luigi Negrini(Inscripton Manager)
Disponível em “http://3.143.69.156/wiki/index.php?title=Conversation_Code_III&oldid=91579”