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.

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

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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.

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馃敟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.
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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!
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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

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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
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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鈥檚 Data Science certification course in Python is a good fit for the below professionals:

路 Programmers, Developers, Technical Leads, Architects

路 Developers aspiring to be a 鈥楳achine Learning Engineer’

路 Analytics Managers who are leading a team of analysts

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

路 Information Architects who want to gain expertise in Predictive Analytics

路 ‘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

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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

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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鈥 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鈥檚 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 鈥楧ata 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鈥

———————————

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.

———————————

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.

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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
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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

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This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

This tutorial was developed by Edureka.

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This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We’ll also implement a neural network manually. Finally, we’ll code a neural network in Python using TensorFlow.

Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

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Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearningWithPython #DeepLearningTutorial #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn鈥檚 Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you鈥檒l build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

With Simplilearn鈥檚 Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-with-Python-fcD6YeEYKNg&utm_medium=Tutorials&utm_source=youtube

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Welcome to part 4 of the Google Cloud tutorial series. In this part, we’re going to explore some of the Natural Language API. We’re going to focus on the entity recognition and sentiment analysis, but you can also do syntactical analysis with this API.

As usual, you will need to both enable this API and of course have the API credentials setup as we did in Part 2.

From here, things should begin to look familiar with the APIs, for example we’ll have client = language.Client(), and then we’ll get all sorts of methods that we can do with some input, which, in this case, will be text.

Sample code: https://pythonprogramming.net/natural-language-api-google-cloud-tutorial/
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The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements.

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

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

Guido van Rossum is the creator of Python, one of the most popular and impactful programming languages in the world. This conversation is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone. Audio podcast version is available on https://lexfridman.com/ai/

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This is the eighth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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This is the ninth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

Sentdex.com
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Alice Zhao

https://pyohio.org/2018/schedule/presentation/38/

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I’m working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP.

Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning.

We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

## Setup Instructions
[ https://github.com/adashofdata/nlp-in-python-tutorial](https://github.com/adashofdata/nlp-in-python-tutorial)

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A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world.

This is the seventh video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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This is the sixth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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This is the tenth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

Sentdex.com
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This is the fifth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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Finally, the moment we’ve all been waiting for and building up to. A live test!

We’ve decided to employ this classifier to the live Twitter stream, using Twitter’s API.

We’ve already covered how to do live Twitter API streaming, if you missed it, you can catch up here: http://pythonprogramming.net/twitter-api-streaming-tweets-python-tutorial/

After this, we output the findings to a text file, which we intend to graph!

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

sample code: http://pythonprogramming.net
http://hkinsley.com
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http://seaofbtc.com

This is the fourth video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.

In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.

Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hard-coding it.

https://pythonprogramming.net/machine-learning-tutorial-python-introduction/
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Watch this Python tutorial to learn Python programming for machine learning and web development.
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猸怣y Favorite Python Books
– Python Crash Course: https://amzn.to/2GqMdjG
– Automate the Boring Stuff with Python: https://amzn.to/2N71d6S
– A Smarter Way to Learn Python: https://amzn.to/2UZa6lE
– Machine Learning for Absolute Beginners: https://amzn.to/2Gs0koL
– Hands-on Machine Learning with scikit-learn and TensorFlow: https://amzn.to/2IdUuJy

TABLE OF CONTENT

00:00:00 Introduction
00:01:49 Installing Python 3
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django

Stay in Touch:
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http://programmingwithmosh.com

This is the third video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

Sentdex.com
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This is the second video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

Sentdex.com
Facebook.com/sentdex
Twitter.com/sentdex

One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to “clean up” and prepare your data for analysis.

One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with.

The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list.

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

Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.

The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.

NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!

Bottom line, if you’re going to be doing natural language processing, you should definitely look into NLTK!

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

Sample code for this series: http://pythonprogramming.net/image-recognition-python/

There are many applications for image recognition. One of the largest that people are most familiar with would be facial recognition, which is the art of matching faces in pictures to identities. Image recognition goes much further, however. It can allow computers to translate written text on paper into digital text, it can help the field of machine vision, where robots and other devices can recognize people and objects.

Here, our goal is to begin to use machine learning, in the form of pattern recognition, to teach our program what text looks like. In this case, we’ll use numbers, but this could translate to all letters of the alphabet, words, faces, really anything at all. The more complex the image, the more complex the code will need to become. When it comes to letters and characters, it is relatively simplistic, however.

How is it done? Just like any problem, especially in programming, we need to just break it down into steps, and the problem will become easily solved. Let’s break it down!

First, we know we want to show the program an image, and have it compare it to patterns that it knows to make an educated guess on what the current image is. This means we’re going to need some “memory” of sorts, filled with examples. In the case of this tutorial, we’d like to do image recognition for the numbers zero through nine. So we’d like to be able to show it any random 2, and have it know the image to be a 2 based on the previous examples of 2’s that it has seen and memorized.

Next, we need to consider how we’ll do this. A computer doesn’t read text like we read text. We naturally put things together into a pattern, but a machine just reads the data. In the case of a picture, it reads in the image data, and displays, pixel by pixel, what it is told to display. Past that, a machine makes no attempt to decide whether it is showing a couch or a bird. So, our database of what examples are will actually be pixel information. To keep things simple, we should probably “threshold” the images. This means we store everything as black or white. In RGB code, that’s a 255, 255, 255, or 0, 0, 0. That is per pixel. Sometimes there is alpha too! What we can then do is take any image, and, if the pixel coloring is say greater than 125, we could say, this is more of a “white” and convert it to 255 (the entire pixel). If it is less than 125 or equal to it, we could say this is more of a “black” and convert it to black. This might be problematic in some circumstances where we have a dark color on a darker color, usually a type of image meant to fool machines. We could have something in place instead to find the “middle” color on average for the current image, and threshold anything lighter to white and anything darker to black. This works very well for two-dimensional images of things like characters, but less well for things with shading that are meant to accompany the image, say of something like a ball.

Once we’ve done this, all we need to do is save the string of pixel definitions for a bunch of “example” texts. We can start with a bunch of fonts, plus some hand drawn examples. There are data dumps of a bunch of examples. This is an example of “training” our data.

If we have a decently sized database, then we are ready to try to compare some numbers. A good idea would be to hand-draw an example for your program to compare to. To compare, we’d just simply do the same thing to the question-image. We’d threshold the image into black or white pixels, then we take that pixel list, and compare it to all of our examples. In the end, we will have so many possible “hits.” Whichever character has the most “hits” is likely to be the correct one. Done, we’ve recognized that image.

If you think about it, this is actually very similar to how we humans recognize things. Naturally, many children do not immediately distinguish between couches and love seats. What is the difference many of them ask. There is a bit of a grey area between them, and they have many similarities. Generally, a lot of learning comes by example. After seeing hundreds of couches, thousands of chairs, and hundreds of love-seats, a person soon begins to easily distinguish between them, because they have quite a bit of sample data to compare to. This is even how we read text. A number 5 really does mean nothing to a baby. They only begin to learn what a number 5 is as they are shown it over and over, being told it is “5.” Eventually, they understand that to be a 5, and they can see 5 in multiple font types and still recognize it to be a 5.

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This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on “AI vs Machine Learning vs Deep Learning” talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:

1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning

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