1 seaborn introduction
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This document introduces the Seaborn library for statistical data visualization in Python. It discusses how Seaborn builds on Matplotlib and Pandas to provide higher-level visualization functions. Specifically, it covers using distplot to create histograms and kernel density estimates, regplot for scatter plots and regression lines, and lmplot for faceted scatter plot grids. Examples are provided to illustrate customizing distplot, combining different plot elements, and using faceting controls in lmplot.
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![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Matplotlib
matplotlib provides the raw building blocks for Seaborn's
visualizations
It can also be used on its own to plot data
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv("wines.csv")
fig, ax = plt.subplots()
ax.hist(df['alcohol'])](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-3-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Pandas
pandas is a foundational library for analyzing data
It also supports basic plotting capability
import pandas as pd
df = pd.read_csv("wines.csv")
df['alcohol'].plot.hist()](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-4-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Seaborn
The distplot is similar to the histogram shown in previous
examples
By default, generates a Gaussian Kernel Density Estimate (KDE)
import seaborn as sns
sns.distplot(df['alcohol'])](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-6-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Histogram vs. Distplot
Pandas histogram
df['alcohol'].plot.hist()
Actual frequency of
observations
No automatic labels
Wide bins
Seaborn distplot
sns.distplot(df['alcohol'])
Automatic label on x axis
Muted color palette
KDE plot
Narrow bins](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-7-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Creating a histogram
Distplot function has multiple optional arguments
In order to plot a simple histogram, you can disable the kde and
specify the number of bins to use
sns.distplot(df['alcohol'], kde=False, bins=10)](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-10-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Alternative data distributions
A rug plot is an alternative way to view the distribution of data
A kde curve and rug plot can be combined
sns.distplot(df_wines['alcohol'], hist=False, rug=True)](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-11-320.jpg)
![INTERMEDIATE DATA VISUALIZATION WITH SEABORN
Further Customizations
The distplot function uses several functions including
kdeplot and rugplot
It is possible to further customize a plot by passing arguments to
the underlying function
sns.distplot(df_wines['alcohol'], hist=False,
rug=True, kde_kws={'shade':True})](https://image.slidesharecdn.com/1seabornintroduction-200621150856/85/1-seaborn-introduction-12-320.jpg)
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What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...Simplilearn This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Machine Learning: Introduction to Neural Networks
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1 seaborn introduction
- 1. Introduction to Seaborn IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN Chris Mof tt Instructor
- 2. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Python Visualization Landscape The python visualization landscape is complex and can be overwhelming
- 3. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Matplotlib matplotlib provides the raw building blocks for Seaborn's visualizations It can also be used on its own to plot data import matplotlib.pyplot as plt import pandas as pd df = pd.read_csv("wines.csv") fig, ax = plt.subplots() ax.hist(df['alcohol'])
- 4. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Pandas pandas is a foundational library for analyzing data It also supports basic plotting capability import pandas as pd df = pd.read_csv("wines.csv") df['alcohol'].plot.hist()
- 5. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Seaborn Seaborn supports complex visualizations of data It is built on matplotlib and works best with pandas' dataframes
- 6. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Seaborn The distplot is similar to the histogram shown in previous examples By default, generates a Gaussian Kernel Density Estimate (KDE) import seaborn as sns sns.distplot(df['alcohol'])
- 7. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Histogram vs. Distplot Pandas histogram df['alcohol'].plot.hist() Actual frequency of observations No automatic labels Wide bins Seaborn distplot sns.distplot(df['alcohol']) Automatic label on x axis Muted color palette KDE plot Narrow bins
- 8. Let's practice! IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN
- 9. Using the distribution plot IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN Chris Mof tt Instructor
- 10. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Creating a histogram Distplot function has multiple optional arguments In order to plot a simple histogram, you can disable the kde and specify the number of bins to use sns.distplot(df['alcohol'], kde=False, bins=10)
- 11. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Alternative data distributions A rug plot is an alternative way to view the distribution of data A kde curve and rug plot can be combined sns.distplot(df_wines['alcohol'], hist=False, rug=True)
- 12. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Further Customizations The distplot function uses several functions including kdeplot and rugplot It is possible to further customize a plot by passing arguments to the underlying function sns.distplot(df_wines['alcohol'], hist=False, rug=True, kde_kws={'shade':True})
- 13. Let's practice! IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN
- 14. Regression Plots in Seaborn IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN Chris Mof tt Instructor
- 15. INTERMEDIATE DATA VISUALIZATION WITH SEABORN Introduction to regplot The regplot function generates a scatter plot with a regression line Usage is similar to the distplot The data and x and y variables must be de ned sns.regplot(x="alcohol", y="pH", data=df)
- 16. INTERMEDIATE DATA VISUALIZATION WITH SEABORN lmplot() builds on top of the base regplot() regplot - low level sns.regplot(x="alcohol", y="quality", data=df) lmplot - high level sns.lmplot(x="alcohol", y="quality", data=df)
- 17. INTERMEDIATE DATA VISUALIZATION WITH SEABORN lmplot faceting Organize data by colors ( hue ) sns.lmplot(x="quality", y="alcohol", data=df, hue="type") Organize data by columns ( col ) sns.lmplot(x="quality", y="alcohol", data=df, col="type")
- 18. Let's practice! IN TERMEDIATE DATA VIS UALIZ ATION W ITH S EABORN