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⚠️ Python Daily Facts ⚠️

Named tuples structure (create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable).

>>> import collections
>>> Point = collections.namedtuple('Point', ['x', 'y'])
>>> p = Point(x=1.0, y=2.0)
>>> p
Point(x=1.0, y=2.0)
>>> p.x
1.0
>>> p.y
2.0

- @pythonpool -

Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.(FREE)

https://developers.google.com/machine-learning/crash-course


👉https://www.tgoop.com/PythonPool

This book is widely considered to the "Bible" of Deep Learning. Written by three experts, including one of the godfathers of the field, this is the most comprehensive book you can find. The book is quite technical but the authors do a great job at explaining everything you need to know to get started.

⚠️ Python Daily Facts ⚠️

Creating an iterator that computes the function using arguments obtained from the iterable of iterables

>>> import itertools
>>> import operator
>>> a = [(2, 6), (8, 4), (7, 3)]
>>> list(itertools.starmap(operator.mul, a))
[12, 32, 21]

- @pythonpool -

⚠️ Python Daily Facts ⚠️

For-else construct useful when searched for something and find it.

# For example assume that I need to search through a list and process each item until a flag item is found and
# then stop processing. If the flag item is missing then an exception needs to be raised.

for i in mylist:
if i == theflag:
break
process(i)
else:
raise ValueError("List argument missing terminal flag.")

- @pythonpool -

⚠️ Daily Python Facts ⚠️

Set comprehension.

>>> m = {x ** 2 for x in range(5)}
>>> m
{0, 1, 4, 9, 16}

- @pythonpool -

⚠️ Python Daily Facts ⚠️

Basic set operations.

>>> A = {1, 2, 3, 3}
>>> A
set([1, 2, 3])
>>> B = {3, 4, 5, 6, 7}
>>> B
set([3, 4, 5, 6, 7])
>>> A | B
set([1, 2, 3, 4, 5, 6, 7])
>>> A & B
set([3])
>>> A - B
set([1, 2])
>>> B - A
set([4, 5, 6, 7])
>>> A ^ B
set([1, 2, 4, 5, 6, 7])
>>> (A ^ B) == ((A - B) | (B - A))
True

- @pythonpool -

List Of Free Online Courses On Artificial Intelligence

1.Google – Machine Learning Udacity

https://in.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187

2.Learn with Google AI

https://ai.google/education/

3.Stanford University – Machine Learning

https://www.coursera.org/learn/machine-learning?ranMID=40328&ranEAID=vByl4oOx0*E&ranSiteID=vByl4oOx0.E-YDZCO8YA4sXKQ1sM6vsMbA&siteID=vByl4oOx0.E-YDZCO8YA4sXKQ1sM6vsMbA&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=vByl4oOx0*E

4.Columbia University – Machine Learning

https://www.edx.org/course/machine-learning-1?source=aw&awc=6798_1531430449_ca710f22668423c745fb884c5f19e637


5.MIT – Deep Learning for Self Driving Cars

https://selfdrivingcars.mit.edu

6.Reinforcement Learning

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

7.Deep Reinforcement Learning (UC Berkeley)

http://rail.eecs.berkeley.edu/deeprlcourse/

8.Machine Learning Crash Course Google

https://developers.google.com/machine-learning/crash-course/

9.Elements of Artificial Intelligence free online course

https://www.elementsofai.com

10.Intro to Artificial Intelligence by Udacity

https://in.udacity.com/course/intro-to-artificial-intelligence--cs271

Note: If you find some more AI courses which are free then please feel free to send us via email. Our email is [email protected]

Do Share our Channel @pythonpool ❤️ For more

⚠️ Daily Python Facts ⚠️

Unpack variables from iterable without indicating all elements.

>>> a, *b, c = [1, 2, 3, 4, 5]
>>> a
1
>>> b
[2, 3, 4]
>>> c
5

- @pythonpool -

For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

Overview

Explore how to analyze your data in various innovative ways and turn them into insightLearn to use the D3.js visualization tool for exploratory data analysisUnderstand how to work with graphs and social data analysisDiscover how to perform advanced query techniques and run MapReduce on MongoDB

In Detail

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.

Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.

Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB.

Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.

⚠️ Daily Python Facts ⚠️

Creating an iterator that filters elements from iterable returning only those for which the predicate is False

>>> import itertools
# keeping only false values
>>> list(itertools.filterfalse(bool, [None, False, 1, 0, 10]))
[None, False, 0]

- @pythonpool -

Deep learning cheatsheets, covering content in Stanford’s CS 230 class.

CNN: https://lnkd.in/daezQBR
RNN: https://lnkd.in/dAUukhR
TipsAndTricks: https://lnkd.in/d4uEU_f

⚠️ Daily Python Facts ⚠️

Unpack variables using the splat operator.

>>> def test(x, y, z):
>>> print(x, y, z)
>>> res = test(*[10, 20, 30])
10 20 30
>>> res = test(**{'x': 1, 'y': 2, 'z': 3} )
10 20 30

- @pythonpool -

Google just announced their new TensorFlow Developer Certificate, which is a great way to showcase your TF skills.

The goal of this certificate is to provide everyone in the world the opportunity to showcase their expertise in ML in an increasingly AI-driven global job market. This certificate in TensorFlow development is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow.
Check it out : https://www.tensorflow.org/certificate

⚠️ Python Daily Facts ⚠️

Flatten iterables.

>>> import itertools
>>> a = [[1, 2], [3, 4], [5, 6]]
>>> list(itertools.chain.from_iterable(a))
[1, 2, 3, 4, 5, 6]

- @pythonpool -

Top free Data Science resources

1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html

2. Data Science Essentials
https://www.edx.org/course/data-science-essentials

3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse

4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI

5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY

6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM

7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/

8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx

10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE