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Natural Language Processing Bootcamp in Python

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Ivo Bernardo

18:05:59

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  • 1 - Course Scripts.txt
  • 1 - DareData Website.txt
  • 1 - Introduction.mp4
    06:51
  • 1 - Ivos Github.txt
  • 1 - LinkedIn Ivo.txt
  • 2 - Note Course is being Updated during 2023.mp4
    02:19
  • 3 - IMPORTANT LECTURE Dont skip this one.mp4
    01:37
  • 4 - Course Materials and Speed Up.html
  • 5 - Link to slides.txt
  • 5 - Slides Setting up the Environment.mp4
    06:17
  • 6 - Anaconda Distribution Download Link.txt
  • 6 - Installing the Anaconda Distribution.mp4
    05:26
  • 7 - Importing an Environment to Anaconda.mp4
    04:26
  • 8 - Creating an Environment from Scratch and Installing Individual Libraries.mp4
    07:04
  • 9 - Alternative Running Notebooks on Google Colab.mp4
    03:52
  • 10 - Link to Slides.txt
  • 10 - Slides Python Basics Course Part 1.mp4
    09:29
  • 11 - Link to Slides.txt
  • 11 - Slides Python Basics Course Part 2.mp4
    07:10
  • 12 - Getting Started Jupyter Notebook Overview.mp4
    15:09
  • 13 - Using Python as a Calculator Exploring Integers and Floats.mp4
    12:53
  • 14 - Exploring Python Libraries Modules Using the Math Library.mp4
    11:46
  • 14 - Python Math Library Documentation.txt
  • 15 - Python Strings and Indexes.mp4
    12:09
  • 16 - Python Lists.mp4
    06:10
  • 17 - Discussing Methods and the Mutability Property.mp4
    06:36
  • 18 - Python Sets.mp4
    03:01
  • 19 - Python Dictionaries.mp4
    02:40
  • 20 - Python Tuples.mp4
    01:54
  • 21 - Control Flow If Statements.mp4
    05:11
  • 22 - Control Flow Python Loops.mp4
    04:19
  • 23 - Python Functions.mp4
    07:14
  • 24 - Numpy Cheat Sheet.txt
  • 24 - Numpy Documentation.txt
  • 24 - Numpy Overview.mp4
    07:30
  • 25 - Pandas Cheat Sheet.txt
  • 25 - Pandas Documentation.txt
  • 25 - Pandas Overview.mp4
    05:29
  • 26 - Tutorial How to Complete the Exercises.mp4
    02:22
  • 27 - Exercise Solutions Code Along Lecture.mp4
    15:55
  • 3 - Working with Text Quiz.html
  • 28 - Slides Basic Text Processing.mp4
    04:41
  • 29 - Why Computers dont understand words as we do.mp4
    06:51
  • 30 - String Indexing.mp4
    05:30
  • 31 - Combining Strings.mp4
    02:17
  • 32 - Iterating Strings and Format Method.mp4
    08:03
  • 33 - Testing if String is in Sentence and Escaping Characters.mp4
    10:16
  • 34 - String Methods 1 Sentence Length Conversions Casing Methods and IsAlpha.mp4
    05:34
  • 35 - String Methods 2 Split Strip and Join.mp4
    06:07
  • 36 - String Methods 3 Capitalize Replace Count and Find.mp4
    07:44
  • 37 - Exercise Solutions Code Along Lecture String Basics.mp4
    09:42
  • 38 - Link to Slides.txt
  • 38 - Slides NLTK Intro Tokenizers and Text Normalization.mp4
    08:43
  • 39 - Link to Slides.txt
  • 39 - Slides NLTK POS and NGrams.mp4
    07:33
  • 40 - Natural Language Toolkit Library.txt
  • 40 - Section Introduction.mp4
    03:49
  • 41 - Intro to Tokenization and Sentence Tokenizer.mp4
    12:43
  • 41 - Natural Language Toolkit Library.txt
  • 41 - Punkt Sentence Tokenizer.txt
  • 42 - TreeBank Word Tokenizer.txt
  • 42 - Word Tokenizer Example.mp4
    09:09
  • 43 - Cleaning our Tokens Removing Punctuation and lowercase.mp4
    09:38
  • 44 - FreqDist NLTK Function.mp4
    07:01
  • 45 - Introduction to Stemming Porter Lancaster and Snowball Stemmer.mp4
    11:46
  • 45 - Porter Stemmer Documentation.txt
  • 45 - Stemming Corpus Medium Article.txt
  • 46 - Stemming Application Example.mp4
    10:47
  • 47 - Introduction to Lemmatization.mp4
    10:10
  • 48 - PartofSpeech POS Tagging.mp4
    11:37
  • 48 - Perceptron POS Tagging Original Blog Post.txt
  • 49 - Training our own POS Tagger Part 1.mp4
    14:05
  • 50 - Training our own POS Tagger Part 2 UniGram Tagger.mp4
    12:36
  • 51 - Training our own POS Tagger Part 3 BiGram Tagger.mp4
    10:08
  • 52 - Lemmatization and POS Tagging.mp4
    14:24
  • 53 - Stop Words.mp4
    10:12
  • 54 - NGrams Concept.mp4
    07:08
  • 55 - Exercise Solutions Code Along Lecture NLTK.mp4
    20:01
  • 56 - Project Description Analyzing IMDB Reviews.mp4
    03:54
  • 57 - Link to the Project Materials.html
  • 58 - Slides Word Vectors Intuition.mp4
    11:05
  • 59 - Introduction to Word Vectors and creating OneHot Vectors.mp4
    11:51
  • 59 - Word Vectors Intuition Medium Article.txt
  • 60 - Initializing CoOccurence Matrix.mp4
    07:59
  • 61 - Filling CoOccurence Matrix.mp4
    14:09
  • 62 - Cosine Similarity Explanation.txt
  • 62 - Exploring Cosine Similarity.mp4
    09:12
  • 63 - Visualizing Word Vectors.mp4
    05:15
  • 64 - Exercise Solutions Code Along Lecture 1 Word Vectors.mp4
    12:55
  • 65 - Exercise Solutions Code Along Lecture 2 Word Vectors.mp4
    08:04
  • 66 - Link to slides.txt
  • 66 - Slides Reading Text Data into Python.mp4
    04:29
  • 67 - Disaster Tweets Source Code.txt
  • 67 - Read Data from a CSV File Using Pandas.mp4
    09:13
  • 68 - Read Data from a CSV File Using Python CSV.mp4
    04:31
  • 69 - Paper where the data is based on.txt
  • 69 - Read Data from a TXT File.mp4
    05:48
  • 70 - 1984 Wikipedia Page.txt
  • 70 - Scraping a Web Page using Requests and BeautifulSoup Wikipedia Example.mp4
    13:57
  • 71 - Scraping a Web Page using Requests and BeautifulSoup Yahoo Finance Example.mp4
    14:57
  • 72 - Scraping a Web Page Errors in Request.mp4
    03:37
  • 73 - Scraping a Web Page using Specific Libraries.mp4
    03:26
  • 74 - Link to slides.txt
  • 74 - Slides Neural Network Definition and Word2Vec.mp4
    21:06
  • 75 - Continuous Bag of Words Model CBOW Introduction.mp4
    11:55
  • 75 - Word2Vec Original Paper.txt
  • 76 - CBOW Creating Vocab and OneHot Vectors.mp4
    11:04
  • 77 - CBOW Building Features X and Target Variable y.mp4
    14:39
  • 78 - Neural Network Introduction and Diagram.mp4
    06:57
  • 79 - A cool intuition on Neural Networks.txt
  • 79 - CBOW Training the Neural Network.mp4
    13:09
  • 80 - CBOW Obtaining Word Vectors Embeddings.mp4
    08:40
  • 81 - Extracting Wikipedia Data for CBOW Model.mp4
    10:03
  • 82 - Building Context from Wikipedia Data.mp4
    13:15
  • 83 - Turning Word and Context Into Mathematical Vectors.mp4
    09:23
  • 84 - Fitting Neural Network on Wikipedia Data.mp4
    09:46
  • 85 - Accuracy of a Model Intuition.txt
  • 85 - Performance of Neural Network and Predicting a Word Given a Context.mp4
    05:22
  • 86 - Retrieving Word Embeddings and Word Similarities.mp4
    10:11
  • 87 - Gensim Library.txt
  • 87 - Loading the Word2VecModel.mp4
    05:38
  • 88 - Word2Vec Operations with Vectors Analogies.mp4
    09:18
  • 89 - Word2Vec Visualizing Vectors using PCA.mp4
    07:35
  • 90 - Exercise Solutions Code Along Lecture Word Vectors using Neural Networks.mp4
    19:40
  • 91 - Project Description The Python Archaelogist.mp4
    02:18
  • 92 - Link to Project Materials.html
  • 93 - Slides Text Representation.mp4
    11:38
  • 93 - Text Representation Medium Article.txt
  • 94 - Disaster Tweets Page.txt
  • 94 - Reading the Tweets File.mp4
    05:02
  • 95 - Binary Vectorizer.mp4
    16:19
  • 95 - Count Vectorizer Document.txt
  • 96 - Count Vectorizer.mp4
    04:40
  • 97 - TFIDF Vectorizer.mp4
    10:53
  • 98 - Creating Document Vectors via Word Embeddings.mp4
    10:17
  • 99 - Exercise Solutions Text Representation Part 1.mp4
    14:04
  • 100 - Exercise Solutions Text Representation Part 2.mp4
    11:14
  • 101 - Slides Text Classification.mp4
    12:56
  • 102 - Intro to Text Classification and Loading Positive Negative Reviews into Python.mp4
    11:19
  • 103 - PreProcessing Text for Text Classification.mp4
    08:57
  • 104 - Exploratory Data Analysis Log Ratio and Word Influence.mp4
    16:36
  • 105 - Stemming and Vectorizing the Reviews.mp4
    14:02
  • 106 - Creating WordVec Features and Target Array.mp4
    08:18
  • 107 - Logistic Regression Intuition and Manually Tweaking Weights.mp4
    17:38
  • 108 - Train and Test Split.mp4
    05:38
  • 109 - Fitting and Evaluating Model.mp4
    06:39
  • 110 - Obtaining the WeightsCoefficients of Regression Influence of Tokens.mp4
    04:50
  • 111 - Training Model Using Word2Vec Features.mp4
    07:25
  • 112 - Confusion Matrix Example.mp4
    10:04
  • 113 - Naive Bayes Training.mp4
    05:01
  • 114 - Predicting New Reviews Sentiment.mp4
    10:02
  • 115 - Exercise Solutions Text Classification.mp4
    19:31
  • 116 - Slides Text Classification.mp4
    09:46
  • 117 - Introduction to Text Generation Tokenizing Sentence.mp4
    10:07
  • 118 - Text Generation Building the Transition Matrix.mp4
    12:52
  • 119 - First Attempt at Generating Text Using Transition Matrix.mp4
    07:59
  • 120 - Creating the Transition Matrix for Wikipedia Data.mp4
    07:23
  • 121 - Generating Text from Wiki Data Sampling from Top N Words.mp4
    10:26
  • 122 - Exercise Solutions Text Generation.mp4
    16:11
  • 123 - Bonus Lecture Other Courses.html
  • 124 - Course Feedback.html
  • 125 - Thank you.mp4
    00:47
  • Description


    Learn the fundamentals of Text Mining and NLP using Text Processing, NLTK, Sentiment Analysis and Neural Networks

    What You'll Learn?


    • Dealing with Strings in Python
    • Working with the Natural Language Toolkit Library
    • Understanding the Intuition behind Word Vectors
    • Pre-Processing Text for Analytics
    • Understanding Text Vectorization
    • Train a Neural Network to generate Word Embeddings
    • Obtain Text Data from Web Pages
    • Read Files with Textual Data
    • Developing a Sentiment Analysis Tool
    • Train a Machine Learning Model

    Who is this for?


  • Beginner Python Developers
  • Experienced Python Developers Interested in learning NLP
  • Data Engineers
  • Data Scientists
  • Business Analysts
  • What You Need to Know?


  • Internet Access
  • Computer with at least 4 GB of RAM
  • More details


    Description

    Welcome aboard your inaugural voyage into the vibrant world of Natural Language Processing (NLP) and Text Mining! This course offers a risk-free foray (backed by a 30-day refund policy) into the fundamental concepts that serve as the bedrock for the text data operations of tech giants like Google, Amazon, and Microsoft.


    Text mining has become a cornerstone of modern Data Science and Analytics. The profound leap in technology that allows a machine to understand words and phrases has revolutionized tasks like Information Retrieval, Translation, and Text Classification. I'm here to help you navigate these waters and jump from the foundational aspects of classical NLP into the misterious realms of Generative AI Tools (such as ChatGPT).


    Our journey will take us from the classical to the neural, exploring the evolution of language processing techniques. We'll begin with traditional statistical methods and work our way up to the cutting-edge world of deep learning and neural networks. By linking theory with practical exercises, I hope to guide you through the NLP World.


    Don't fret if Python isn't your forte yet - included in this course is a crash course in Python that will acquaint you with the language and provide the necessary foundation for the rest of the topics we'll cover.

    The course will illuminate a variety of key NLP concepts including:


    1. Manipulating the basic building blocks of NLP - strings - in Python;

    2. Tokenizing Sentences and Documents;

    3. Stemming and Lemmatizing words;

    4. Training machine learning models using text;

    5. Extracting the Part-of-Speech Tag from words in a sentence;

    6. Extracting Text Data from a Web Page;

    7. Training a Neural Network to extract Word Embeddings;

    8. Developing your own sentiment classifier (Sentiment Analysis);

    9. Representing Sentences as Tabular Data;

    Upon completing this course, you'll be equipped with the skills to construct your own basic NLP applications, and you'll have a strong understanding of the fundamental concepts underlying most NLP algorithms. This knowledge will open doors to more advanced studies in NLP, while providing an understanding of the strategies and techniques utilized by companies when launching their NLP applications.

    Embark on this exhilarating journey through the world of NLP with me. Whether you're a newcomer or an expert seeking to broaden your horizons, there's a place for you here. I'm eagerly looking forward to our adventure together in the course!

    Who this course is for:

    • Beginner Python Developers
    • Experienced Python Developers Interested in learning NLP
    • Data Engineers
    • Data Scientists
    • Business Analysts

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    Ivo Bernardo
    Ivo Bernardo
    Instructor's Courses
    Ivo Bernardo is a professional with a passion for Data Science and Analytics and is currently a Partner at DareData Engineering, a startup that implements machine learning systems all around world for companies of different sizes (from startups to large enterprises). He holds a master Degree in Statistics and Business Intelligence from New University of Lisbon and has been an instructor in several data science academies throughout the years. His main teaching passion is helping beginners or professionals from other industries taking their first leap into the Data Science and Analytics space. Technically he has worked with Python, R, SQL and main cloud providers infrastructure. Feel free to contact him on LinkedIn or via Udemy for potential business ventures or collaborations._________________________________________________________________________Ivo Bernardo é um profissional com grande interesse por Data Science e Analytics. De momento, é Partner da DareData Engineering, uma startup que implementa sistemas de machine learning em todo o mundo para empresas de diferentes tamanhos (desde startups a grandes organizações). É mestre em Estatística e Business Intelligence pela Universidade Nova de Lisboa e participou como instructor em algumas academias data science em Portugal. A sua principal paixão enquanto instrutor é ajudar iniciantes ou profissionais de outras áreas a dar o seu primeiro passo no mundo de Data Science e Analytics. Tecnicamente, trabalha maioritariamente com Python, R, SQL e com a infraestrutura das plataformas cloud mais relevantes do mundo. Pode contactá-lo directamente no LinkedIn ou via Udemy para potenciais colaborações ou outros assuntos relacionados com Data Science.
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 120
    • duration 18:05:59
    • English subtitles has
    • Release Date 2024/05/18

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