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2020-12-13 06:52:35
30-Day Money-Back Guarantee

This course includes:

  • 28 hours on-demand video
  • 2 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Natural Language Processing

2021 Natural Language Processing in Python for Beginners

Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing
Rating: 4.5 out of 54.5 (179 ratings)
1,425 students
Created by Laxmi Kant
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn complete text processing with Python
  • Learn how to extract text from PDF files
  • Use Regular Expressions for search in text
  • Use SpaCy and NLTK to extract complete text features from raw text
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Use Scikit-Learn and Deep Learning for Text Classification
  • Learn Multi-Class and Multi-Label Text Classification
  • Use Spacy and NLTK for Sentiment Analysis
  • Understand and Build word2vec and GloVe based ML models
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies
  • Learn Text Summarization and Text Generation using LSTM and GRU
Curated for the Udemy for Business collection

Course content

22 sections • 275 lectures • 27h 55m total length

  • Preview08:29
  • Course Overview
    05:25
  • DO NOT SKIP IT | Resources Folder
    00:18
  • Install Anaconda and Python 3 on Windows 10
    06:04
  • Install Anaconda and Python 3 on Ubuntu Machine
    03:24
  • Install Anaconda and Python 3 on Mac Machine
    06:37
  • Install Git Bash and Commander Terminal
    06:51
  • Jupyter Notebook Shortcuts
    09:09

  • Preview01:22
  • Data Types
    07:00
  • Variable Assignment
    05:25
  • String Assignment
    07:51
  • List
    04:16
  • Set
    03:47
  • Tuple
    02:40
  • Dictionary
    03:55
  • Boolean and Comparison Operator
    03:36
  • Logical Operator
    04:07
  • If, Else, Elif
    06:42
  • Loops in Python
    06:13
  • Methods and Lambda Function
    05:31

  • Introduction
    00:55
  • Array
    10:34
  • NaN and INF
    07:43
  • Statistical Operations
    03:28
  • Shape, Reshape, Ravel, Flatten
    02:50
  • Sequence, Repetitions, and Random Numbers
    12:36
  • Where(), ArgMax(), ArgMin()
    03:55
  • File Read and Write
    06:28
  • Concatenate and Sorting
    03:26
  • Working with Dates
    03:06

  • Introduction
    00:55
  • DataFrame and Series
    05:15
  • File Reading and Writing
    08:27
  • Info, Shape, Duplicated, and Drop
    05:20
  • Columns
    03:12
  • NaN and Null Values
    05:36
  • Imputation
    05:06
  • Lambda Function
    05:22

  • Introduction to NLP
    04:34
  • Install Spacy
    04:43
  • Introduction to Spacy
    08:05
  • Tokenization
    06:34
  • Parts of Speech [POS] Tagging
    04:50
  • Dependency Visualization
    03:53
  • Preview04:52
  • Sentence Segmentation
    02:31
  • Rule Based Phrase Matching
    11:12
  • Regular Expression Part 1
    09:39
  • Regular Expression Part 2
    05:30
  • Processing Pipeline in Spacy
    13:10
  • Preview14:33

  • String Formatting
    08:37
  • Working with open() Files in write() Mode Part 1
    06:53
  • Working with open() Files in write() Mode Part 2
    06:29
  • Working with open() Files in write() Mode Part 3
    02:19
  • Read and Evaluate the Files
    09:01
  • Reading and Writing .CSV and .TSV Files with Pandas
    09:02
  • Reading and Writing .XLSX Files with Pandas
    07:16
  • Preview08:01
  • Reading Files from URL Links
    01:43
  • Preview08:56
  • Record the Audio and Convert to Text
    09:54
  • Convert Audio in Text Data
    09:32
  • Text to Speech Generation
    04:04

  • Introduction
    07:38
  • Word Counts
    05:35
  • Characters Counts
    05:10
  • Preview02:56
  • Stop Words Count
    06:06
  • Count #hashtag and @mentions
    04:32
  • Numeric Digit Count
    04:24
  • Upper case Words Count
    03:53
  • Lower case Conversion
    03:05
  • Contraction to Expansion
    07:59
  • Count and Remove Emails
    09:03
  • Count and Remove URLs
    10:10
  • Remove RT from Tweeter Data
    03:54
  • Special Chars Removal and Punctuation Removal
    03:20
  • Remove Multiple Spaces
    01:45
  • Remove HTML Tags
    04:07
  • Remove Accented Chars
    03:00
  • Remove Stop Words
    02:30
  • Convert into Base or Root Form of Words
    07:03
  • Common Words Removal
    06:06
  • Rare Words Removal
    02:11
  • Word Cloud Visualization
    03:45
  • Preview02:21
  • Tokenization with TextBlob
    01:56
  • Nouns Detection
    01:29
  • Language Translation and Detection
    02:33
  • Sentiment Prediction with TextBlob
    03:06

  • Code Files Setup
    06:20
  • Preview06:16
  • Setup.py Preparation
    09:29
  • Utils.py Code Along Part 1
    07:53
  • Utils.py Code Along Part 2
    07:39
  • Utils.py Code Along Part 3
    08:35
  • Utils.py Code Along Part 4
    10:18
  • __init__.py Code Along
    13:27
  • GitHub Account Setup and Package Upload
    10:28
  • SSH Key Setup for GitHub
    05:52
  • Install Preprocess Python Package
    05:40
  • Removing the Errors Part 1
    04:05
  • Removing the Errors Part 2
    14:12
  • Testing the Package
    04:45

  • Logistic Regression Intuition
    08:19
  • Support Vector Machine Intuition
    07:01
  • Decision Tree Intuition
    05:25
  • Random Forest Intuition
    03:33
  • L2 Regularization
    08:07
  • L1 Regularization
    04:38
  • Model Evaluation Metrics: Accuracy, Precision, Recall, and Confusion Matrix
    08:05
  • Model Evaluation Metrics: ROC and AUC
    03:26
  • Code Along in Python Part 1
    06:36
  • Code Along in Python Part 2
    07:02
  • Code Along in Python Part 3
    06:33
  • Preview11:07

  • Text Feature Extraction Intuition Part 1
    06:35
  • Text Feature Extraction Intuition Part 2
    09:38
  • Bag of Words (BoW) Code Along in Python
    05:25
  • Term Frequency (TF) Code Along in Python
    06:33
  • Inverse Document Frequency (IDF) Code Along in Python
    08:04
  • TFIDF Code Along in Python
    04:23
  • Load Spam Dataset
    04:28
  • Balance Dataset
    03:50
  • Exploratory Data Analysis (EDA)
    05:14
  • Data Preparation for Training
    08:16
  • Build and Train SVM and Random Forest Models
    08:53
  • Preview02:20

Requirements

  • Have a desire to learn
  • Elementary level math
  • Have basic understanding of Python and Machine Learning

Description

Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge cutting NLP problems.

In this course, we will start from level 0 to the advanced level.

We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing.

In the next section, we will learn about working with Files for storing and loading the text data. This section is the foundation of another section on Complete Text Preprocessing. I will show you many ways of text preprocessing using Spacy and Regular Expressions. Finally, I will show you how you can create your own python package on preprocessing. It will help us to improve our code writing skills. We will be able to reuse our code systemwide without writing codes for preprocessing every time. This section is the most important section.

Then, we will start the Machine learning theory section and a walkthrough of the Scikit-Learn Python package where we will learn how to write clean ML code. Thereafter, we will develop our first text classifier for SPAM and HAM message classification. I will be also showing you various types of word embeddings used in NLP like Bag of Words, Term Frequency, IDF, and TF-IDF. I will show you how you can estimate these features from scratch as well as with the help of the Scikit-Learn package.

Thereafter we will learn about the machine learning model deployment. We will also learn various other important tools like word2vec, GloVe, Deep Learning, CNN, LSTM, RNN, etc.

At the end of this lesson, you will learn everything which you need to solve your own NLP problem.









Who this course is for:

  • Beginners in Natural Language Processing
  • Data Scientist curious to learn NLP

Instructor

Laxmi Kant
Principal Data Scientist at mBreath and KGPTalkie
Laxmi Kant
  • 4.6 Instructor Rating
  • 977 Reviews
  • 30,687 Students
  • 6 Courses

I am a Principal Data Scientist at SleepDoc and a Ph.D. in Data Science from the Indian Institute of Technology (IIT). I had also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNC. I have also taught programming at IIT for few years and then later started a YouTube channel, KGP Talkie with 20K+ subscribers. I am very well connected with industry and academia.

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