Install and setup environment

avic783 / TensorFlow-Pokemon-Course

https://lab.github.com/everydeveloper/advance-tensorflow

1 stars 0 forks source link

Install and setup environment #1

Open github-learning-lab[bot] opened 3 years ago

github-learning-lab[bot] commented 3 years ago

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

avic783 commented 3 years ago

This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

github-learning-lab[bot] commented 3 years ago

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns
>>> Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
       'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',
       'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',
       'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',
       'Body_Style'],
      dtype='object')

Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

avic783 commented 3 years ago

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor man i was Beijing serious Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin! First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal: pip install tensorflow Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages: import tensorflow as tf from tensorflow import keras import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing Leave a comment with your favorite Pokémon (such as Pikachu) to continue. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1, or

unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:
df.columns
>>> Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
       'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',
       'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',
       'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',
       'Body_Style'],
      dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:
df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]
Which library did we use to read our CSV file?

Leave a comment with your answer to continue

github-learning-lab[bot] commented 3 years ago

Sorry, "> Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My

supervisor man i was Beijing serious Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin! First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal: pip install tensorflow Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages: import tensorflow as tf from tensorflow import keras import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing Leave a comment with your favorite Pokémon (such as Pikachu) to continue. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1, or

unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:
df.columns
>>> Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
       'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',
       'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',
       'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',
       'Body_Style'],
      dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:
df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]
Which library did we use to read our CSV file?

Leave a comment with your answer to continue

" is not the answer we were looking for.

The correct answer is: "pandas".

avic783 commented 3 years ago

I am so dedicated but not so experienced

Den tors 25 mars 2021 10:58github-learning-lab[bot] < @.***> skrev:

Sorry, "> Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My

supervisor man i was Beijing serious Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin! First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal: pip install tensorflow Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages: import tensorflow as tf from tensorflow import keras import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing Leave a comment with your favorite Pokémon (such as Pikachu) to continue. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or

unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
   'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

   'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

   'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

   'Body_Style'],

  dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

" is not the answer we were looking for.

The correct answer is: "pandas".

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806516348, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHB4BORSGUJVGXGD6BDTFMCLNANCNFSM4ZY7SLAQ .

github-learning-lab[bot] commented 3 years ago

Sorry, "I am so dedicated but not so experienced

Den tors 25 mars 2021 10:58github-learning-lab[bot] < @.***> skrev:

Sorry, "> Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My

supervisor man i was Beijing serious Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin! First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal: pip install tensorflow Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages: import tensorflow as tf from tensorflow import keras import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing Leave a comment with your favorite Pokémon (such as Pikachu) to continue. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or

unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
   'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

   'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

   'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

   'Body_Style'],

  dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

" is not the answer we were looking for.

The correct answer is: "pandas".

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806516348, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHB4BORSGUJVGXGD6BDTFMCLNANCNFSM4ZY7SLAQ .

" is not the answer we were looking for.

The correct answer is: "pandas".

avic783 commented 3 years ago

Hahahhahahahahahahahha

Den tors 25 mars 2021 10:50github-learning-lab[bot] < @.***> skrev:

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or

unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
   'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

   'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

   'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

   'Body_Style'],

  dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806512162, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHA6VTG6BFCJTC64NSLTFMBQDANCNFSM4ZY7SLAQ .

github-learning-lab[bot] commented 3 years ago

Sorry, "Hahahhahahahahahahahha

Den tors 25 mars 2021 10:50github-learning-lab[bot] < @.***> skrev:

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or

unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',
   'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

   'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

   'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

   'Body_Style'],

  dtype='object')
Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806512162, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHA6VTG6BFCJTC64NSLTFMBQDANCNFSM4ZY7SLAQ .

" is not the answer we were looking for.

The correct answer is: "pandas".

avic783 commented 3 years ago

Ey yo Whats pip

Den tors 25 mars 2021 11:06github-learning-lab[bot] < @.***> skrev:

Sorry, "Hahahhahahahahahahahha

Den tors 25 mars 2021 10:50github-learning-lab[bot] < @.***> skrev:

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1 #1

https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',

'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

'Body_Style'],

dtype='object')

Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

— You are receiving this because you commented. Reply to this email directly, view it on GitHub

1 (comment)

https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806512162 , or unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHA6VTG6BFCJTC64NSLTFMBQDANCNFSM4ZY7SLAQ .

" is not the answer we were looking for.

The correct answer is: "pandas".

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806521733, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHCQ6UHX33OF75HDO6LTFMDJXANCNFSM4ZY7SLAQ .

github-learning-lab[bot] commented 3 years ago

Sorry, "Ey yo Whats pip

Den tors 25 mars 2021 11:06github-learning-lab[bot] < @.***> skrev:

Sorry, "Hahahhahahahahahahahha

Den tors 25 mars 2021 10:50github-learning-lab[bot] < @.***> skrev:

Wow, "This what i wannna talk abiut i have a meetinf 1 time a weekly with My supervisor

Den tors 25 mars 2021 10:40github-learning-lab[bot] < @.***> skrev:

Welcome to the world of machine learning with TensorFlow! Working with TensorFlow can seem intimidating at first, but this tutorial will start with the basics to ensure you have a strong foundation with the package. This tutorial will be focusing on classifying and predicting Pokémon, but the elements discussed within it can certainly be helpful when using TensorFlow for other ideas, as well. Without further ado, let's begin!

First, let's download TensorFlow through pip. While you can install the version of TensorFlow that uses your GPU, we'll be using the CPU-driven TensorFlow. Type this into your terminal:

pip install tensorflow

Now that it's installed, we can truly begin. Let's import Tensorflow, and a few other packages we'll need. All of this course involve using the command line interface. Enter these commands to import and the necessary packages:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn import preprocessing

Leave a comment with your favorite Pokémon (such as Pikachu) to continue.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub

1 https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1 #1

https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1, or unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHF4AA2ZRI3P6IGYFYDTFMAJFANCNFSM4ZY7SLAQ .

" is also my favorite!

The dataset we'll be using is the compilation of stats and traits for the Pokémon video games. Pokémon is a popular game for generations of Nintendo handheld video game consoles where players collect and train animal-like creatures called Pokémon. We'll be creating a model to try to predict whether a Pokémon is a legendary Pokémon, a rare type of Pokémon who's the only one of its species.

There are a lot of existing compilations of Pokémon stats, but we'll be using a .CSV version found on Kaggle https://www.kaggle.com/alopez247/pokemon. There's a download button on the website, so save the file to your computer and we can begin.

First, we need to read in the CSV file. We'll be doing so using Pandas:

df = pd.read_csv('/path/to/file/pokemon.csv')

First, let's see what the categories of data are. This was also available on the Kaggle page, but that won't be the case for most real-world data:

df.columns

Index(['Number', 'Name', 'Type_1', 'Type_2', 'Total', 'HP', 'Attack',

'Defense', 'Sp_Atk', 'Sp_Def', 'Speed', 'Generation', 'isLegendary',

'Color', 'hasGender', 'Pr_Male', 'Egg_Group_1', 'Egg_Group_2',

'hasMegaEvolution', 'Height_m', 'Weight_kg', 'Catch_Rate',

'Body_Style'],

dtype='object')

Okay so we have a lot of types of data here! Some of these descriptions might be confusing to those who aren't very familiar with the games. That's okay, we'll narrow our focus a little and only select categories we think will be relevant. It's always nice to have more data to train the model with, but it also takes time to clean and prepare that data. We'll be keeping it simple here:

df = df[['isLegendary','Generation', 'Type_1', 'Type_2', 'HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed','Color','Egg_Group_1','Height_m','Weight_kg','Body_Style']]

Which library did we use to read our CSV file?

Leave a comment with your answer to continue

— You are receiving this because you commented. Reply to this email directly, view it on GitHub

1 (comment)

https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806512162 , or unsubscribe

https://github.com/notifications/unsubscribe-auth/ATMWSHA6VTG6BFCJTC64NSLTFMBQDANCNFSM4ZY7SLAQ .

" is not the answer we were looking for.

The correct answer is: "pandas".

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/avic783/TensorFlow-Pokemon-Course/issues/1#issuecomment-806521733, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATMWSHCQ6UHX33OF75HDO6LTFMDJXANCNFSM4ZY7SLAQ .

" is not the answer we were looking for.

The correct answer is: "pandas".