PyData LA 2018

I will present some way in which tensor methods can be combined with deep learning, and demonstrate through Jupyter notebooks on how easy it is specify tensorized neural networks.

www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Build A Virtual Assistant Using Python

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Learn Artificial Intelligence from leading experts and attain a Dual Certificate in AI and Machine Learning from world-renowned universities. Take the step towards your professional growth by obtaining expertise in the real-world application of the latest technological tools of AI. Over 500+ Hiring Partners & 8000+ career transitions over varied domains.
Know More: https://glacad.me/3qSjmt0

For data sets, code files and projects associated with course please enroll for free at: https://www.greatlearning.in/academy/learn-for-free/courses/machine-learning-with-python
Machine learning is changing the world that we live in. Top companies such as Facebook, Google, Microsoft and Amazon are looking for machine learning engineers and the average salary of a machine learning engineer is around 120k$ dollars.

Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. https://glacad.me/3uBbddU

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This full course on Machine Learning with Python will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.

He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).

These are the topics covered in this session:

-Agenda – 0:00
-Introduction to Python and Anaconda – 3:58
-Introduction to Pandas and Data Manipulation – 1:07:05
-Introduction to Numpy and Numerical Computing – 4:42:32
-Data Visualization – 5:10:58
-Statistics vs Machine Learning – 6:06:12
-Types of Statistics – 6:12:44
-Understanding Data – 7:54:39
-What is Reinforcement Learning? – 7:58:19
-Reinforcement Learning Framework – 8:53:46
-Q-Learning – 9:24:58
-Case Study on Smart Taxi – 9:51:08
————————————————————————————————————
Read more on Machine Learning
https://www.mygreatlearning.com/blog/what-is-machine-learning/

You can check out our other full course videos:

Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=17638s

Data Science full Course: https://www.youtube.com/watch?v=u2zsY-2uZiE&t=979s

Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=2s

Time Series Analysis: https://www.youtube.com/watch?v=FPM6it4v8MY&t=8433s

Probability and Statistics: https://www.youtube.com/watch?v=z9siRCCElls&t=4844s

Machine Learning Salary Trends in India : https://www.mygreatlearning.com/blog/machine-learning-salary-in-india/

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Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.

⭐️ Google Colaboratory Notebooks ⭐️

📕 Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true
📗 Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true
📘 Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true
📙 Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true
📔 Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true
📒 Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true

⭐️ Course Contents ⭐️

⌨️ (00:03:25) Module 1: Machine Learning Fundamentals
⌨️ (00:30:08) Module 2: Introduction to TensorFlow
⌨️ (01:00:00) Module 3: Core Learning Algorithms
⌨️ (02:45:39) Module 4: Neural Networks with TensorFlow
⌨️ (03:43:10) Module 5: Deep Computer Vision – Convolutional Neural Networks
⌨️ (04:40:44) Module 6: Natural Language Processing with RNNs
⌨️ (06:08:00) Module 7: Reinforcement Learning with Q-Learning
⌨️ (06:48:24) Module 8: Conclusion and Next Steps

⭐️ About the Author ⭐️

The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below:
🔗 YouTube: https://www.youtube.com/channel/UC4JX40jDee_tINbkjycV4Sg
🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/

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This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

Programmers can get anything for free, even Alexa. Building your own (hot) personal assistant — how cool that is! 😁 You just need 20 lines of Python code. 😎

Code: https://github.com/ProgrammingHero1/romantic-alexa

If you face any coding related issues or anything, just join our Discord server and we’re waiting for you: https://discord.gg/abk4RTjJjC

#python #hack #alexa #crush #special #romantic #pythonhack #amazona #amazonalexa #programming #fun #project

WHAT IS THE VIDEO ABOUT?
Save your money and build your own Alexa.
Make her do anything, yeah anything. She is all yours.

VIDEO TIMESTAMPS
0:27 – Grandma’s new drama
0:35 – Creating project file
1:45 – Installing necessary package
3:10 – Let’s start coding

3:10 – Alexa listening
7:33 – Alexa voice return
16:47 – Install pywhatkit package
19:30 – Working with datetime
21:30 – Install python wikipedia
25:46 – Asking romantic questions
27:03 – Install pyjokes
29:35 – Final test and ready!

Now, if you’re new to the programming world and don’t know what to do, go check out our app and build your own game immediately while learning.
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If you like and subscribe to this cool video, she’ll be ready to be in a relationship with you. 😉

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Machine Learning Python Weather Prediction

07:02

In this video I give machine learning with python a go. And I build a machine learning model for predicting the weather in the future.
If you want to learn machine learning I do attempt to explain how machine learning works at 7:02 in the video. As kind of an intro to machine learning.

Articles ranked from most useful to useful but less useful:
1. https://nbviewer.jupyter.org/github/srnghn/ml_example_notebooks/blob/master/Predicting%20Yacht%20Resistance%20with%20Decision%20Trees%20%26%20Random%20Forests.ipynb?source=post_page—————————

2. https://elitedatascience.com/python-machine-learning-tutorial-scikit-learn

3. https://machinelearningmastery.com/make-predictions-scikit-learn/

Github repo: https://github.com/KalleHallden/WeatherPredictor

“Clean Code Friday”
If you want to receive one short email from me every week, where I go through a few of the most useful things I have explored and discovered this week. Things like; favourite apps, articles, podcasts, books, coding tips and tricks. Then feel free to join https://kalletech.com/clean-code-friday/

CONTACT: contact@kalletech.com

Follow me on:

TWITCH: https://www.twitch.tv/kallehallden
INSTAGRAM: https://www.instagram.com/kallehallden/
TWITTER: https://twitter.com/kallehallden
GITHUB: https://github.com/kallehallden

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Promising new research on kidneys:
https://wonderfulengineering.com/this-artificial-kidney-eliminates-the-need-for-kidney-dialysis/?fbclid=IwAR19Olftqa9vr0HEhwl51c89OPN6RGsxPNDDJB2rzhqdKkIVGYYXeGCF1Ds

Mayo Clinic Website on Chronic Kidney Disease:
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🔵Edureka Python Programming Certification Course (Use Code “𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎”): https://www.edureka.co/python-programming-certification-training
This Edureka video on Python Full Course will help you learn the Python programming language and its core concepts with examples from scratch. Below are the topics covered in this Python tutorial video:
00:00 Introduction
01:55 What is Python?
03:40 Why is Python popular?
04:55 Features of Python
6:50 Where is Python used in the industry?
7:50 Learning Path
9:15 Career Opportunities
10:30 How Netflix use Python?
11:55 How does it use Python?
18:15 Python Developer Salary
18:50 Who is Python Developer?
19:20 Python Developer Job Trends
21:55 How to become a Python Developer?
23:05 Who is a Python developer?
33:50 Job Roles
41:40 Emerging Job Roles
44:30 Road Map
47:05 Python Installation
54:30 How to run a Python program?
58:35 Best IDE for Python
59:35 What is an IDE?
59:50 Features of an IDE
1:00:50 Best IDEs of Python
1:07:40 PyCharm Tutorial
1:07:42 Introduction to PyCharm
1:09:07 Features of PyCharm
1:28:02 Comments in Python
1:28:52 What are Comments?
1:29:47 When to use Python?
1:30:22 How to write comments in Python?
1:32:22 Types of Comments
1:35:57 Docstring Comments
1:39:02 Variables & Data Types
1:39:17 Variable definition & Declaration
1:41:22 Data Types
1:41:42 Numbers
1:43:12 String
1:46:02 List
1:48:12 Dictionary
1:50:12 Tuple
1:51:27 Set
1:53:52 Type Conversion
1:54:42 Python Collections
1:57:12 Specialised Collections Data Types
2:14:57 Arrays
2:15:32 What is an Array?
2:17:27 How to create Arrays in Python?
2:20:52 Accessing Array Elements
2:22:42 Basic Array Operations
2:24:27 Adding elements to an Array
2:27:52 Removing Elements of an Array
2:33:42 Slicing an Array
2:36:12 Looping through an Array
2:39:57 Hash Table and HashMap
2:41:37 Creating Dictionaries
2:44:02 Nested dictionaries
2:46:52 Performing Operations on Hash Table
2:55:32 Operators in Python
3:13:17 Loops in Python
3:13:52 Why to use Python?
3:16:12 What are Loops?
3:18:07 Loops in Python
3:18:17 While Loop
3:26:22 For Loop
3:32:22 Nested Loop
3:48:12 Patterns in Python
4:24:27 File Handling
4:25:07 Why need File Handling
4:27:02 Types of Files
4:28:37 What is File Handling?
4:29:22 Python File Handling System
4:32:52 File Operations for Reading
4:41:27 Python File Write Method
4:47:37 Decorator
4:48:22 Functions in Python
4:54:07 Decorators in Python
5:08:32 Lambda
5:10:17 How to write Anonymous functions?
5:29:02 Map-Reduce Functions
5:29:47 Introduction to map(), filter(), reduce()
5:46:22 What are Generators?
5:47:32 Normal functions vs Generators
5:48:02 Writing Generators in Python
5:52:57 Generators with Loops
5:53:57 Generator Expressions
5:56:22 Use-Cases
6:02:27 OOPS Concepts
6:03:07 Classes & Objects
6:33:47 Inheritance in Python
6:36:52 Types of Inheritance
6:42:47 Python Super Function
6:44:52 Exception Handling
6:54:42 Try & Except block
7:05:52 Python Module
7:07:47 How to create a Module?
7:17:33 Python Modules Search Path
7:24:23 Date & Time
7:24:58 time module
7:38:23 Numpy
7:38:58 What is Numpy?
7:41:43 Numpy vs List
7:49:03 Numpy Operations
8:05:23 Numpy Special Functions
8:09:58 SciPy
8:10:48 What is Python SciPy?
8:12:23 Basic Functions
8:15:48 Special Functions
8:18:53 Integration Functions
8:24:43 Linear Algebra
8:26:48 Interpolation Functions
8:28:18 Pandas
8:29:18 Data life-cycle
8:31:23 What is Pandas?
8:35:33 Pandas Operations
8:54:13 Example
8:59:38 Python for Statistics
9:02:43 Pydoop
9:04:03 Matplotlib
9:04:28 Data Visualization
9:09:13 Matplotlib
9:10:13 Types of Plots
9:30:13 Multiple Plots
9:32:18 Seaborn
9:32:53 Introduction to Seaborn
9:33:33 Seaborn vs Matplotlib
9:34:28 How to install Seaborn?
9:34:48 Installing Dependencies
9:35:33 Python Seaborn Functions
9:44:23 Multi-Plot Grids
9:47:08 Plot Aesthetics
9:50:58 FIFA Use-case
10:15:33 OpenCV Tutorial
10:16:03 What is Computer Vision?
10:16:48 How computer reads an Image?
10:18:53 What is OpenCV?
10:19:58 Basic operations with OpenCV
10:27:33 Face Detection
10:27:38 Face Detection using OpenCV
10:31:53 Capturing Video
10:43:28 Use-case – Motion Detector
10:54:58 Web Development Framework
10:55:23 What are Frameworks in Python?
11:08:34 Python Web Development
11:09:04 Python Web Development Libraries
11:09:24 Python & Django
11:28:19 Web Scraping
11:33:49 Packages used for web Scraping

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Got a question on the topic? Share in the comments.

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Hi. In this lecture will transform tokens into features. And the best way to do that is Bag of Words. Let’s count occurrences of a particular token in our text. The motivation is the following. We’re actually looking for marker words like excellent or disappointed, and we want to detect those words, and make decisions based on absence or presence of that particular word, and how it might work. Let’s take an example of three reviews like a good movie, not a good movie, did not like. Let’s take all the possible words or tokens that we have in our documents. And for each such token, let’s introduce a new feature or column that will correspond to that particular word. So, that is a pretty huge metrics of numbers, and how we translate our text into a vector in that metrics or row in that metrics. So, let’s take for example good movie review. We have the word good, which is present in our text. So we put one in the column that corresponds to that word, then comes word movie, and we put one in the second column just to show that that word is actually seen in our text. We don’t have any other words, so all the rest are zeroes. And that is a really long vector which is sparse in a sense that it has a lot of zeroes. And for not a good movie, it will have four ones, and all the rest of zeroes and so forth. This process is called text vectorization, because we actually replace the text with a huge vector of numbers, and each dimension of that vector corresponds to a certain token in our database. You can actually see that it has some problems. The first one is that we lose word order, because we can actually shuffle over words, and the representation on the right will stay the same. And that’s why it’s called bag of words, because it’s a bag they’re not ordered, and so they can come up in any order. And different problem is that counters are not normalized. Let’s solve these two problems, and let’s start with preserving some ordering. So how can we do that? Actually you can easily come to an idea that you should look at token pairs, triplets, or different combinations. These approach is also called as extracting n-grams. One gram stands for tokens, two gram stands for a token pair and so forth. So let’s look how it might work. We have the same three reviews, and now we don’t only have columns that correspond to tokens, but we have also columns that correspond to let’s say token pairs. And our good movie review now translates into vector, which has one in a column corresponding to that token pair good movie, for movie for good and so forth. So, this way, we preserve some local word order, and we hope that that will help us to analyze this text better. The problems are obvious though. This representation can have too many features, because let’s say you have 100,000 words in your database, and if you try to take the pairs of those words, then you can actually come up with a huge number that can exponentially grow with the number of consecutive words that you want to analyze. So that is a problem. And to overcome that problem, we can actually remove some n-grams. Let’s remove n-grams from features based on their occurrence frequency in documents of our corpus. You can actually see that for high frequency n-grams, as well as for low frequency n-grams, we can show why we don’t need those n-grams. For high frequency, if you take a text and take high frequency n-grams that is seen in almost all of the documents, and for English language that would be articles, and preposition, and stuff like that. Because they’re just there for grammatical structure and they don’t have much meaning. These are called stop-words, they won’t help us to discriminate texts, and we can pretty easily remove them. Another story is low frequency n-grams, and if you look at low frequency n-grams, you actually find typos because people type with mistakes, or rare n-grams that’s usually not seen in any other reviews. And both of them are bad for our model, because if we don’t remove these tokens, then very likely we will overfeed, because that would be a very good feature for our future classifier that can just see that, okay, we have a review that has a typo, and we had only like two of those reviews, which had those typo, and it’s pretty clear whether it’s positive or negative. So, it can learn some independences that are actually not there and we don’t really need them. And the last one is medium frequency n-grams, and those are really good n-grams, because they contain n-grams that are not stop-words, that are not typos and we actually look at them. And, the problem is there’re a lot of medium frequency n-grams. And it proved to be useful to look at n-gram frequency in our corpus for filtering out bad n-grams. What if we can use the same frequency for ranking of medium frequency n-grams?

🔥 Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka video on ‘If Else In Python’ will help you understand how you can use a conditional if and else statements in python for decision making with concepts like shorthand if and else, nested if else etc. Following are the topics discussed:
0:52 – What Are Python Conditions?
1:45 – What Is If And If Else In Python?
3:08 – Syntax For If Else In Python
9:58 – Shorthand If Else
12:13 – Use Case – Nested If Else

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How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – – – – –
About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions and using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

– – – – – – – – – – – – – – – – – – –

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects
· Developers aspiring to be a ‘Machine Learning Engineer’
· Analytics Managers who are leading a team of analysts
· Business Analysts who want to understand Machine Learning (ML) Techniques
· Information Architects who want to gain expertise in Predictive Analytics
· ‘Python’ professionals who want to design automatic predictive models

For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll free)

Here we go over a Python Project using OpenCV and simple Machine Learning
Google Colab Link : https://colab.research.google.com/drive/1DOvXJZRkjfKfF9oUpCZXSU3hPG3g1h-l?usp=sharing
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This video contains python implementation of Realtime Face Emotion Recognition
1) Brainstorming (background of facial emotion recognition)
(i)Challenges in FER 2013 dataset
2) OpenCV for drawing rectangles and overlaying text data
3) Face emotion recognition using DeepFace library
4) Live Video demo using OpenCV + DeepFace for Webcam

Deep learning, Deepfake, Machine Learning, Classification algorithm, Regression Algorithm, Supervised ML, Clustering Algorithm,Business Intelligence (BI),Data Engineering,Decision Science,Artificial Intelligence (AI),Machine Learning,Supervised Learning,Classification,Clustering,Deep Learning,Linear Regression,A/B Testing,Hypothesis Testing,Statistical Power,Statistical Power,Standard Error,Exploratory Data Analysis (EDA),Data Visualization,R, Python, SQL, GitHub, ETL, Data Models,

In this video we will be using the Python Face Recognition library to do a few things

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Examples & Docs:
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https://github.com/ageitgey/face_recognition

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** Edureka Python Certification Training: https://www.edureka.co/python **
This Edureka video on ‘Robot Framework With Python’ explains the various aspects of robot framework in python with a use case showing web testing using selenium library. Following are the topics discussed in this Robot Framework Tutorial:

What is Robot Framework In Python?
Standard Libraries
Built-in Tools
Test Cases
Keywords
Variables
Organizing Test Cases
Use Case – RobotFramework-SeleniumLibrary

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#PythonEdureka #Edureka #robotframeworkinpython #pythonprojects #pythonprogramming #pythontutorial #PythonTraining

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How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – – – – –
About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
– – – – – – – – – – – – – – – – – – –

Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects

· Developers aspiring to be a ‘Machine Learning Engineer’

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand Machine Learning (ML) Techniques

· Information Architects who want to gain expertise in Predictive Analytics

· ‘Python’ professionals who want to design automatic predictive models

For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free

This python AI project will teach you how to make a virtual assistant like iron man Jarvis in Python.
►Source Code: https://codewithharry.com/videos/python-tutorials-for-absolute-beginners-120
⌚TimeStamps:
00:00 – Introduction
01:30 – Python Course Information
02:35 – Jarvis AI Basic Details
05:45 – Jarvis AI Demo
09:36 – Downloading VS Code & Python IDLE
10:08 – Starting VS Code (Coding begins here!)
10:54 – Defining Speak Function – This Function will program your Jarvis to speak something.
15:25 – Defining Wishme Function – This Function will make your Jarvis wish you according to system time.
18:27 – Defining Take command Function – This Function will allow your Jarvis to take microphone input from the user and returns a string output.
27:30 – Coding logic of Jarvis
28:04 – Defining Task 1: To search something on Wikipedia
31:24 – Defining Task 2: To open YouTube site in browser
32:34 – Defining Task 3 : To open Google site in browser
33:37 – Defining Task 4 : To play music
36:58 – Defining Task 5 : To know current time
38:45 – Defining Task 6 : To open VS Code Program
41:05 – Defining Task 7 : To send E-mail
44:03 – Declaring Send email function – It will define all crucial things like to whom you wanna send an email and its content.
50:52 – Free Python Course Details
51:26 – Recapitulate
56:13 – Is it an AI?
58:30 – The END

This Python personal assistant tutorial will properly teach you how to create J.A.R.V.I.S with Python which is a Voice Activated Desktop Assistant. This Python AI virtual Assistant Tutorial will get you kick started and move towards the world of AI and ML.

►Full Python tutorials for absolute beginners (Hindi) playlist – https://www.youtube.com/playlist?list=PLu0W_9lII9agICnT8t4iYVSZ3eykIAOME
►Click here to subscribe – https://www.youtube.com/channel/UCeVMnSShP_Iviwkknt83cww

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►Object Oriented Programming In Python – https://www.youtube.com/playlist?list=PLu0W_9lII9ahfRrhFcoB-4lpp9YaBmdCP

►Python Data Science and Big Data Tutorials – https://www.youtube.com/playlist?list=PLu0W_9lII9agK8pojo23OHiNz3Jm6VQCH

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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we’re ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.

To start, we’re going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we’ll be doing, positive sentiment or negative sentiment.

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

In this video we’ll be modifying our chat app so that whenever the server goes offline it can automatically be replaced by another peer in the network. This means our chat app will, in theory, be able to run indefinitely.

Go to https://howcode.org for more!

Link to DigitalOcean: http://howco.de/d_ocean

Link to howCode Facebook: http://howco.de/fb
Link to howCode Twitter: http://howco.de/twitter
Link to /r/howCode: http://howco.de/reddit

Don’t forget to subscribe for more!

Hello! Today I will show you how to make image recognition bots as fast as possible using Python. I will cover the basics of Pyautogui, Python, win32api and by the end, you should be able to make a bot for pretty much any game.

Here are the commands to run and code to paste: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial/blob/master/README.txt

All code can be found here: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial

If this video helped you please consider subscribing and leaving a like, it helps a ton!

If you have any errors/suggestions please let me know!

Discord server: https://discord.com/invite/8NcumxN

🔥NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka “Stock Prediction using Machine Learning” takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc. Topics covered in the tutorial are as follow:
01:30 Introduction to Stock Prediction
03:00 LSTM Architecture
05:35 Stock Prediction Model
26:35 Conclusion

🔸Dataset & code: https://bit.ly/2x6BPe0

🔹Check out our machine learning Playlist: https://bit.ly/3byztDp
🔹And our Blog series: https://bit.ly/34YXH7n
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#edureka #machinelearningEdureka #StockPredictionUsingMachineLearning #machinelearningalgorithms #pythonMachineLearning #machineLearningTraining
———————————————-
Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.
———————————————–
PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance
———————————————–
Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking
———————————————–
Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website – Sales Prediction
———————————————–
Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada
——————————————–
Is this program for me?

If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.
——————————————–
For more information, Please write back to us at sales@edureka.in or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

Natural Language Processing techniques allow addressing tasks like text classification and information extraction and content generation. They can give the perception of machines being able to understand humans and respond more naturally.

In this session, Barbara will introduce basic concepts of natural language processing. Using Python and its machine learning libraries the attendees will go through the process of building the bag of words representation and using it for text classification. It can be then used to recognise the sentiment, category or the authorship of the document.
The goal of this tutorial is to build the intuition on the simple natural language processing task. After this session, the audience will know basics of the text representation, learn how to develop the classification model and use it in real-world applications.

NDC Conferences
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This video shows how we can use Break and Continue statements in a loop to effectively handle exceptions.

——————————————————-
*About us*

Applied AI course (AAIC Technologies Pvt. Ltd.) is an Ed-Tech company based out in Hyderabad offering on-line training in Machine Learning and Artificial intelligence. Applied AI course through its unparalleled curriculum aims to bridge the gap between industry requirements and skill set of aspiring candidates by churning out highly skilled machine learning professionals who are well prepared to tackle real world business problems.

*Key highlights of Applied AI course*

1. Job guarantee or money back guarantee
2. Query resolution inside 24 hours
3. Personalized learning path for every course participant
4. 30 Practical Assignments
5. 15 end-to-end case studies based on real world problems across various industries
6. Mentor-ship for portfolio development, resume and interview preparation, and career counseling for every course participant

For More information Please visit https://www.appliedaicourse.com/

For any queries you can either drop a mail to team@appliedaicourse.com or call us at +91 8106-920-029 or +91 6301-939-583

Facebook: https://www.facebook.com/appliedaicou…
Soundcloud: https://soundcloud.com/applied-ai-course
Twitter: https://twitter.com/appliedaicourse

#AppliedAICourse,#Python,#BreakContinueStatements,#ArtificialIntelligence,#MachineLearning,#DeepLearning,#DataScience,#NLP,#AI,#ML

🔥Intellipaat natural language processing in python course: https://intellipaat.com/nlp-training-course-using-python/
In this natural language processing tutorial video you will learn what is natural language, text mining in nlp, file handling in python, nltk package, tokenization, frequency distribution, stop words and the concepts of bi grams, tri grams and n grams in detail.
#NaturalLanguageProcessingNLPinPython #NaturalLanguageProcessingTutorial #NaturalLanguageProcessing #Intellipaat

📌 Do subscribe to Intellipaat channel & get regular updates on videos: https://goo.gl/hhsGWb

📕Read complete AI tutorial here: https://intellipaat.com/tutorial/artificial-intelligence-tutorial/

📔Interested to learn AI still more? Please check similar what is AI blog here: https://intellipaat.com/blog/what-is-artificial-intelligence/

📝Interested to read about AI certificationS? Please check similar blog here: https://intellipaat.com/blog/artificial-intelligence-certification/

🔗Watch complete AI tutorials here: https://bit.ly/2YTKB7u

Are you looking for something more? Enroll in our natural language processing Course and become a certified professional (https://intellipaat.com/nlp-training-course-using-python/). It is a 20 hrs instructor led training provided by Intellipaat which is completely aligned with industry standards and certification bodies.

If you’ve enjoyed this nlp tutorial, Like us and Subscribe to our channel for more similar tutorials.
Got any questions about how natural language processing python works? Ask us in the comment section below.
—————————-
Intellipaat Edge
1. 24*7 Life time Access & Support
2. Flexible Class Schedule
3. Job Assistance
4. Mentors with +14 yrs
5. Industry Oriented Course ware
6. Life time free Course Upgrade
——————————
Why should you watch this natural language processing tutorial?

NLP market is speculated to grow to US$26.4 billion by 2024 with CAGR of 21%. One of the principal disciplines of AI, Natural language processing is used to solve uses analysis tools to read data from large amounts of natural language data to arrive at meaningful conclusions. It involves using the ML algorithms to recognize, categorize, and extract natural language rules to transform unstructured language data into a form that computers can understand.

Why Artificial Intelligence is important?

Artificial Intelligence is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat deep learning training & Artificial Intelligence Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why should you opt for a Artificial Intelligence career?

If you want to fast-track your career then you should strongly consider Artificial Intelligence. The reason for this is that it is one of the fastest growing technology. There is a huge demand for professionals in Artificial Intelligence. The salaries for A.I. Professionals is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Artificial Intelligence tutorial & deep learning tutorial is your stepping stone to a successful career!
——————————
For more Information:
Please write us to sales@intellipaat.com, or call us at: +91- 7847955955
Website: https://intellipaat.com/nlp-training-course-using-python/

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This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

** Python Certification Training: https://www.edureka.co/python **
This Edureka video on ‘Speech Recognition in Python’ will cover the concepts of speech recognition module in python with a program using speech recognition to translate speech into text. Following are the topics discussed:

How Speech Recognition Works?
How To Install SpeechRecognition In Python?
Working With Microphones
How To Install Pyaudio In Python?
Use case

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Reference: https://youtu.be/avH1P41-jh8

#Edureka #PythonEdureka #PythonSpeechrecognition #pythonprojects #pythonprogramming #pythontutorial #speechtotext #speechtotextinpython #PythonTraining

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

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Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

———————————————————————————————————————————–

How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – – – – –
About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
– – – – – – – – – – – – – – – – – – –

Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects

· Developers aspiring to be a ‘Machine Learning Engineer’

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand Machine Learning (ML) Techniques

· Information Architects who want to gain expertise in Predictive Analytics

· ‘Python’ professionals who want to design automatic predictive models

For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406/ US: 18338555775 (toll free)

This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

Hey Guys,
Hope you enjoying my AI tutorials using Keras and Tensorflow.

This is the video for facial emotion recognition using CNN.
Transfer learning is the best way to perform such a complicated task.
For this task, we will classify the emotions from the frame coming directly through your webcam or any external live camera.

This is a realtime emotion detection easy tutorial using python and Keras.

You can use this video as realtime emotion detection using python.

Please do share and subscribe for more interesting videos.

Dataset :- https://drive.google.com/open?id=1E66iZdNz021aUZGsZjtc3EUu3NqAaIq3

Source Code :- https://github.com/code-by-dt/emotion_detection

Facial Landmark Detection OpenCV Too Easy Tutorial https://youtu.be/16bzzVaqKCk

Computer Vision Programs :- https://www.youtube.com/playlist?list=PLgNUGWgXIL4pWASWqSdAvYupEocaktF2D

—————PROGRAMMERS SECTION——————–

➤Follow Me On Git Hub🐈:-https://github.com/code-by-dt
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( **Natural Language Processing Using Python: – https://www.edureka.co/python-natural-language-processing-course ** )
This video will provide you with a detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this video:

0:46 – Introduction to Big Data
1:45 – What is Text Mining?
2:09- What is NLP?
3:48 – Introduction to Stemming
8:37 – Introduction to Lemmatization
10:03 – Applications of Stemming & Lemmatization
11:04 – Difference between stemming & Lemmatization

Subscribe to our channel to get video updates. Hit the subscribe button above https://goo.gl/6ohpTV

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Edureka’s Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed.

This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience.

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Who Should go for this course ?

Edureka’s NLP Training is a good fit for the below professionals:
From a college student having exposure to programming to a technical architect/lead in an organisation
Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Text Mining Techniques
‘Python’ professionals who want to design automatic predictive models on text data
“This is apt for everyone”

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Why Learn Natural Language Processing or NLP?

Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users.

NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.

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For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).

After browsing and trying out over 4 different courses from multiple learning platforms this course from PY4E really stood out. Without any programming knowledge, I used this course to build my own payroll and incentive calculation system for my organization that employs over 100 people. Course Curator Certified Best Python Cousre on the Web. Dr. Charles Severance is truely a gifted educator who can simplify complex topics in to easy to USE, bite sized episodes that help you learn whats needed to start building applications right away!
Please visit https://www.py4e.com/ to get additional information on the course.
You can take this course for a certificate as the Python for Everybody Specialization on Coursera at https://www.coursera.org/specializations/python

To begin, what is regression in terms of us using it with machine learning? The goal is to take continuous data, find the equation that best fits the data, and be able forecast out a specific value. With simple linear regression, you are just simply doing this by creating a best fit line.

From here, we can use the equation of that line to forecast out into the future, where the ‘date’ is the x-axis, what the price will be.

A popular use with regression is to predict stock prices. This is done because we are considering the fluidity of price over time, and attempting to forecast the next fluid price in the future using a continuous dataset.

Regression is a form of supervised machine learning, which is where the scientist teaches the machine by showing it features and then showing it was the correct answer is, over and over, to teach the machine. Once the machine is taught, the scientist will usually “test” the machine on some unseen data, where the scientist still knows what the correct answer is, but the machine doesn’t. The machine’s answers are compared to the known answers, and the machine’s accuracy can be measured. If the accuracy is high enough, the scientist may consider actually employing the algorithm in the real world.

https://pythonprogramming.net
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Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. “The hotel is really beautiful. Very nice and helpful service at the front desk.” So we read that and we understand that is a positive review. As for the negative review, “We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn’t fun for us.” So, it’s easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we’ll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, “I don’t really like”, that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let’s start with words and let’s denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters.

So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let’s look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They’re just written in such form that they don’t have spaces.The Japanese language is a different story.

Part of Speech tagging does exactly what it sounds like, it tags each word in a sentence with the part of speech for that word. This means it labels words as noun, adjective, verb, etc. PoS tagging also covers tenses of the parts of speech.

This is normally quite the challenge, but NLTK makes this pretty darn simple!

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Speech Recognition using Python

Learn how to convert audio into text using python.

Code here : https://github.com/umangahuja1/Youtube/blob/master/Python_Extras/speech.py

Stay Tuned 🙂

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