This document discusses using the Seaborn library in Python for data visualization. It covers installing Seaborn, importing libraries, reading in data, cleaning data, and creating various plots including distribution plots, heatmaps, pair plots, and more. Code examples are provided to demonstrate Seaborn's functionality for visualizing and exploring data.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
In this power point presentation i have explained about Seaborn Library in Data Visualization.
I have touched the topics like Introduction, what is Seaborn types etc.
Hope this ppt will help you & you will like it.
Thank You
All the best
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
In this power point presentation i have explained about Seaborn Library in Data Visualization.
I have touched the topics like Introduction, what is Seaborn types etc.
Hope this ppt will help you & you will like it.
Thank You
All the best
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Analysis of data in Python with SciPy and pandas, Ubuntu installation, PyCharm configuration, Series, DataFrame, big data, medical data, merging data, groupby, graphing data, iPython using Wakari.io, and analyzing stock prices of US automakers including Ford and Telsa. As presented at Penguicon 2016.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Using the following code Install Packages pip install .pdfpicscamshoppe
Using the following code:
##Install Packages
!pip install tensorflow
!pip install matplotlib
!pip install numpy
!pip install pandas
##Import Statements
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
##Bringing in our dataset
url= 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323-
/main/sales_forecast.csv'
data = pd.read_csv(url, sep=',')
##Filtering and Cleaning
data= data[['Store', 'Date', 'Temperature', 'Fuel_Price', 'CPI' , 'Unemployment', 'Weekly_Sales',
'IsHoliday_y']]
data['Date'] = pd.to_datetime(data['Date'], format='%d/%m/%Y')
data = data.set_index('Date')
data = data.sort_index()
##Checklist and Quality Assurance
data.isnull()
print(f'Number of rows with missing values: {data.isnull().any(axis=1).mean()}')
data.info()
##Subsetting variable to predict
df= data['Weekly_Sales']
df.plot()
##Train and Test Split
start_train = datetime(2010, 2, 5)
end_train = datetime(2011, 12, 30)
end_test = datetime(2012, 7, 13)
msk_train = (data.index >= start_train) & (data.index <= end_train)
msk_test = (data.index >= end_train) & (data.index <= end_test)
df_train = df.loc[msk_train]
df_test = df.loc[msk_test]
df_train.plot()
df_test.plot()
##Normalizing our data
uni_data= df.values.astype(float)
df_train= int(len(df_train))
uni_train_mean= uni_data[:df_train].mean()
uni_train_std= uni_data[:df_train].std()
uni_data= (uni_data-uni_train_mean)/uni_train_std
##Build the features dataset to make the model multivariate
features_considered = ['Temperature', 'Fuel_Price', 'CPI']
features = data[features_considered]
features.index = data.index
##Standardizing the Data
dataset = features.values
data_mean = dataset[:df_train].mean(axis=0)
data_std = dataset[:df_train].std(axis=0)
dataset = (dataset-data_mean)/data_std
##Splitting data into training and testing
x_train = dataset[msk_train]
y_train = uni_data[:df_train]
x_test = dataset[msk_test]
y_test = uni_data[df_train:]
##Defining the model architecture
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=[x_train.shape[1]]),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
##Compiling the model
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='mae')
##Fitting the model to the training data
history = model.fit(x_train, y_train,
epochs=100,
batch_size=64,
validation_split=0.2,
verbose=0)
##Evaluating the model on the test data
results = model.evaluate(x_test, y_test, verbose=0)
print(f'Test loss: {results}')
##Making predictions on new data
predictions = model.predict(x_test)
##Plotting the results
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(np.arange(len(y_test)), y_test, label='Actual')
ax.plot(np.arange(len(predictions)), predictions, label='Predicted')
ax.legend()
plt.title('Actual vs Predicted Weekly Sales')
plt.xlabel('Week')
plt.ylabel('Normalized Sales')
plt.show()
*BUILD the RNN*
##Defining Function to Build.
**Exploratory Data Analysis (EDA) Tools:**
Exploratory Data Analysis (EDA) is a crucial step in understanding and making sense of data in data science projects. Various tools and libraries are available to assist in this process, offering features like visualizations, data profiling, and statistical analysis. Here are some popular EDA tools:
1. **DataPrep**:
- Offers interactive visualizations and fast performance due to its Dask-based computing module.
- Suitable for big data analysis and provides insights through comprehensive visualizations.
- Efficient in handling missing values, checking correlations, and data cleansing[1][3].
2. **Pandas-profiling**:
- Popular for its ability to handle large datasets and address data privacy concerns.
- Generates detailed reports with relevant features highlighted for EDA.
- Useful for smaller datasets where privacy is a concern[1][2].
3. **SweetViz**:
- Provides detailed visualizations to understand complex data patterns.
- Offers insights into the dataset through interactive graphs and distribution charts[1].
4. **Lux**:
- Appeals to users comfortable with pandas syntax, offering additional functionality with a simple call.
- Enables users to perform EDA tasks conveniently within the pandas environment[1].
5. **D-Tale**:
- Stands out for its interactive GUI that eliminates the need for coding during EDA tasks.
- Offers a network analyzer for visualizing relationships between factors and responses[1].
These tools cater to different user preferences and requirements, providing a range of functionalities to facilitate effective exploratory data analysis
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Analysis of data in Python with SciPy and pandas, Ubuntu installation, PyCharm configuration, Series, DataFrame, big data, medical data, merging data, groupby, graphing data, iPython using Wakari.io, and analyzing stock prices of US automakers including Ford and Telsa. As presented at Penguicon 2016.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Using the following code Install Packages pip install .pdfpicscamshoppe
Using the following code:
##Install Packages
!pip install tensorflow
!pip install matplotlib
!pip install numpy
!pip install pandas
##Import Statements
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
##Bringing in our dataset
url= 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323-
/main/sales_forecast.csv'
data = pd.read_csv(url, sep=',')
##Filtering and Cleaning
data= data[['Store', 'Date', 'Temperature', 'Fuel_Price', 'CPI' , 'Unemployment', 'Weekly_Sales',
'IsHoliday_y']]
data['Date'] = pd.to_datetime(data['Date'], format='%d/%m/%Y')
data = data.set_index('Date')
data = data.sort_index()
##Checklist and Quality Assurance
data.isnull()
print(f'Number of rows with missing values: {data.isnull().any(axis=1).mean()}')
data.info()
##Subsetting variable to predict
df= data['Weekly_Sales']
df.plot()
##Train and Test Split
start_train = datetime(2010, 2, 5)
end_train = datetime(2011, 12, 30)
end_test = datetime(2012, 7, 13)
msk_train = (data.index >= start_train) & (data.index <= end_train)
msk_test = (data.index >= end_train) & (data.index <= end_test)
df_train = df.loc[msk_train]
df_test = df.loc[msk_test]
df_train.plot()
df_test.plot()
##Normalizing our data
uni_data= df.values.astype(float)
df_train= int(len(df_train))
uni_train_mean= uni_data[:df_train].mean()
uni_train_std= uni_data[:df_train].std()
uni_data= (uni_data-uni_train_mean)/uni_train_std
##Build the features dataset to make the model multivariate
features_considered = ['Temperature', 'Fuel_Price', 'CPI']
features = data[features_considered]
features.index = data.index
##Standardizing the Data
dataset = features.values
data_mean = dataset[:df_train].mean(axis=0)
data_std = dataset[:df_train].std(axis=0)
dataset = (dataset-data_mean)/data_std
##Splitting data into training and testing
x_train = dataset[msk_train]
y_train = uni_data[:df_train]
x_test = dataset[msk_test]
y_test = uni_data[df_train:]
##Defining the model architecture
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=[x_train.shape[1]]),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
##Compiling the model
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='mae')
##Fitting the model to the training data
history = model.fit(x_train, y_train,
epochs=100,
batch_size=64,
validation_split=0.2,
verbose=0)
##Evaluating the model on the test data
results = model.evaluate(x_test, y_test, verbose=0)
print(f'Test loss: {results}')
##Making predictions on new data
predictions = model.predict(x_test)
##Plotting the results
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(np.arange(len(y_test)), y_test, label='Actual')
ax.plot(np.arange(len(predictions)), predictions, label='Predicted')
ax.legend()
plt.title('Actual vs Predicted Weekly Sales')
plt.xlabel('Week')
plt.ylabel('Normalized Sales')
plt.show()
*BUILD the RNN*
##Defining Function to Build.
**Exploratory Data Analysis (EDA) Tools:**
Exploratory Data Analysis (EDA) is a crucial step in understanding and making sense of data in data science projects. Various tools and libraries are available to assist in this process, offering features like visualizations, data profiling, and statistical analysis. Here are some popular EDA tools:
1. **DataPrep**:
- Offers interactive visualizations and fast performance due to its Dask-based computing module.
- Suitable for big data analysis and provides insights through comprehensive visualizations.
- Efficient in handling missing values, checking correlations, and data cleansing[1][3].
2. **Pandas-profiling**:
- Popular for its ability to handle large datasets and address data privacy concerns.
- Generates detailed reports with relevant features highlighted for EDA.
- Useful for smaller datasets where privacy is a concern[1][2].
3. **SweetViz**:
- Provides detailed visualizations to understand complex data patterns.
- Offers insights into the dataset through interactive graphs and distribution charts[1].
4. **Lux**:
- Appeals to users comfortable with pandas syntax, offering additional functionality with a simple call.
- Enables users to perform EDA tasks conveniently within the pandas environment[1].
5. **D-Tale**:
- Stands out for its interactive GUI that eliminates the need for coding during EDA tasks.
- Offers a network analyzer for visualizing relationships between factors and responses[1].
These tools cater to different user preferences and requirements, providing a range of functionalities to facilitate effective exploratory data analysis
Leland Lammert's presentation on Distributed Heirarchical Nagios.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
Building an ML Platform with Ray and MLflowDatabricks
A successful machine learning platform allows ML practitioners to focus solely on their experiments and models and minimizes the time it takes to develop ML applications and take them to production. However, building an ML Platform is typically not an easy task due to the many different components involved in the process. In this talk, we will show how two open source projects, Ray (https://ray.io/) and MLflow (https://mlflow.org/), work together to make it easy for ML platform developers to add scaling and experiment management to their platform.
We will first provide an overview of Ray and its native libraries: Ray Tune (https://tune.io) for distributed hyperparameter tuning and Ray Serve (https://docs.ray.io/en/master/serve/index.html) for scalable model serving. Then we will showcase how MLflow provides a perfect solution for managing experiments through integrations with Ray for tracking and model deployment. Finally, we will finish with a demo of an ML platform built on Ray, MLflow, and other open source tools.
CascadiaJS 2015 - Adding intelligence to your JS applicationsKevin Dela Rosa
My talk from CascadiaJS 2015. Follow me on twitter - @kevd1337
Abstract:
There's a lot of data out there coming in the form of user generated content on the internet, open data from government agencies, speech input from mobile devices, and streams of information available from internet of things devices. Being able to leverage this information to find meaningful signals can help make your app standout and provide great value to your users.
In this talk I'll give a brief overview of basic machine learning concepts and talk about tools available for node.js to accomplish common intelligent systems tasks and motivate these tools through examples. After slogging through the basics we'll go through how these can be pieced together to do more complex tasks.
Presentació a càrrec d'Ismael Fernández i Cristian
Gomollón (tècnics d'Aplicacions al CSUC) duta a terme a la "3a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 29 d'octubre de 2020 en format virtual.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.