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    Natural Language Processing in Python for Beginners

    BY
    Udemy

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    Online

    Fees

    ₹ 449 2999

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹2,999
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Machine Learning Intuition
    • Course Overview
    • DO NOT SKIP IT | Resources Folder!
    • Install Anaconda and Python 3 on Windows 10
    • Install Anaconda and Python 3 on Ubuntu Machine
    • Install Anaconda and Python 3 on Mac Machine
    • Install Git Bash and Commander Terminal
    • Jupyter Notebook Shortcuts

    Python Crash Course

    • Introduction
    • Data Types
    • Variable Assignment
    • String Assignment
    • List
    • Set
    • Tuple
    • Dictionary
    • Boolean and Comparison Operator
    • Logical Operator
    • If, Else, Elif
    • Loops in Python
    • Methods and Lambda Function

    Numpy Introduction [Optional]

    • Introduction
    • Array
    • NaN and INF
    • Statistical Operations
    • Shape, Reshape, Ravel, Flatten
    • Sequence, Repetitions, and Random Numbers
    • Where(), ArgMax(), ArgMin()
    • File Read and Write
    • Concatenate and Sorting
    • Working with Dates

    Pandas Introduction [Optional]

    • Introduction
    • DataFrame and Series
    • File Reading and Writing
    • Info, Shape, Duplicated, and Drop
    • Columns
    • NaN and Null Values
    • Imputation
    • Lambda Function

    Spacy Introduction

    • Must Read This
    • Introduction to NLP
    • Install Spacy
    • Introduction to Spacy
    • Tokenization
    • Parts of Speech [POS] Tagging
    • Dependency Visualization
    • Named Entity Recognition (NER)
    • Sentence Segmentation
    • Rule Based Phrase Matching
    • Regular Expression Part 1
    • Regular Expression Part 2
    • Processing Pipeline in Spacy
    • Hashtags and Emoji Detection

    Working with Text Files

    • String Formatting
    • Working with open() Files in write() Mode Part 1
    • Working with open() Files in write() Mode Part 2
    • Working with open() Files in write() Mode Part 3
    • Read and Evaluate the Files
    • Reading and Writing .CSV and .TSV Files with Pandas
    • Reading and Writing .XLSX Files with Pandas
    • Reading and Writing .JSON Files
    • Reading Files from URL Links
    • Extract Text Data From PDF
    • Record the Audio and Convert to Text
    • Convert Audio in Text Data
    • Text to Speech Generation

    Complete Text Cleaning and Preprocessing

    • Must Read This
    • Introduction
    • Word Counts
    • Characters Counts
    • Average Word Length
    • Stop Words Count
    • Count #hashtag and @mentions
    • Numeric Digit Count
    • Upper case Words Count
    • Lower case Conversion
    • Contraction to Expansion
    • Count and Remove Emails
    • Count and Remove URLs
    • Remove RT from Tweeter Data
    • Special Chars Removal and Punctuation Removal
    • Remove Multiple Spaces
    • Remove HTML Tags
    • Remove Accented Chars
    • Remove Stop Words
    • Convert into Base or Root Form of Words
    • Common Words Removal
    • Rare Words Removal
    • Word Cloud Visualization
    • Spelling Correction
    • Tokenization with TextBlob
    • Nouns Detection
    • Language Translation and Detection
    • Sentiment Prediction with TextBlob

    Text Cleaning and Preprocessing in Python | Software Packaging for PIP Install

    • Code Files Setup
    • Readme and License File Preparation
    • Setup.py Preparation
    • Utils.py Code Along Part 1
    • Utils.py Code Along Part 2
    • Utils.py Code Along Part 3
    • Utils.py Code Along Part 4
    • __init__.py Code Along
    • GitHub Account Setup and Package Upload
    • SSH Key Setup for GitHub
    • Install Preprocess Python Package
    • Removing the Errors Part 1
    • Removing the Errors Part 2
    • Testing the Package

    Introduction to Machine Learning with Scikit-Learn

    • Logistic Regression Intuition
    • Support Vector Machine Intuition
    • Decision Tree Intuition
    • Random Forest Intuition
    • L2 Regularization
    • L1 Regularization
    • Model Evaluation Metrics: Accuracy, Precision, Recall, and Confusion Matrix
    • Model Evaluation Metrics: ROC and AUC
    • Code Along in Python Part 1
    • Code Along in Python Part 2
    • Code Along in Python Part 3
    • Code Along in Python Part 4

    Spam Text Classification

    • Text Feature Extraction Intuition Part 1
    • Text Feature Extraction Intuition Part 2
    • Bag of Words (BoW) Code Along in Python
    • Term Frequency (TF) Code Along in Python
    • Inverse Document Frequency (IDF) Code Along in Python
    • TFIDF Code Along in Python
    • Load Spam Dataset
    • Balance Dataset
    • Exploratory Data Analysis (EDA)
    • Data Preparation for Training
    • Build and Train SVM and Random Forest Models
    • Test Your Model with Real Data

    Real-Time Twitter Sentiment Analysis

    • Notebook Setup
    • SVM Model Training
    • Test Your Model
    • Data Cleaning and Retraining SVM Part 1
    • Data Cleaning and Retraining SVM Part 2
    • Fine Tune Your ML Model
    • Saving and Loading ML Model
    • Create Twitter Developer Account
    • Get the Access Tokens
    • Reading Twitter Timeline in Real-Time
    • Tracking Keywords in Real-Time on Twitter Part 1
    • Tracking Keywords in Real-Time on Twitter Part 2
    • Tracking Keywords in Real-Time on Twitter Part 3
    • Real-Time Sentiment Analysis with TextBlob
    • Real-Time Sentiment Analysis with Trained ML Model
    • Real-Time Twitter Sentiment Analysis of USA vs China Part 1
    • Real-Time Twitter Sentiment Analysis of USA vs China Part 2
    • Real-Time Twitter Sentiment Animation Plot Part 1
    • Real-Time Twitter Sentiment Animation Plot Part 2

    Fine Tuning of ML Algorithms

    • What is Feature Dimensionality Reduction
    • Principal Components Analysis (PCA)
    • Linear Discriminant Analysis (LDA)
    • Non-Negative Matrix Factorization (NMF)
    • Truncated Singular Value Decomposition (TSVD)
    • TF-IDF and Sparse Matrix Part 1
    • TF-IDF and Sparse Matrix Part 2
    • TF-IDF and Sparse Matrix Part 3
    • Non-Negative Matrix Factorization (NMF) Code Along Part 1
    • Non-Negative Matrix Factorization (NMF) Code Along Part 2
    • Truncated Singular Value Decomposition (TSVD) Code Along
    • What is Hyperparameters Tuning
    • Hyperparameter Tuning Methods
    • Grid Search for Hyperparameters with K-Fold Cross-Validation
    • GridSearch for Logistic Regression Hyperparameters Tuning Part 1
    • GridSearch for Logistic Regression Hyperparameters Tuning Part 2
    • GridSearch for SVM Hyperparameters Tuning Part 1
    • GridSearch for SVM Hyperparameters Tuning Part 2
    • Grid Search for Random Forest Classifier Hyperparameters Tuning
    • Random Search for Best Hyperparameters Selection
    • Selecting Best Models from Multiple ML Algorithms

    Sentiment Analysis on IMDB Movie Reviews with TF-IDF Text Embedding

    • How Sentiment is Detected from Text Data
    • Text Preprocessing Package Install
    • Text Cleaning and Preprocessing
    • Data Preparation for Model Training
    • ML Model Building and Training
    • Logistic Regression Model Evaluation
    • Traning and Hyperparameters Tuning of SVM
    • Load and Store ML Model

    ML Model Deployment with Flask

    • Install Flask
    • Run Flask Server
    • Model Preparation with Flask
    • Running Flask App with ML Model Part 1
    • Running Flask App with ML Model Part 2

    Multi-Label Text Classification for Stack Overflow Tag Prediction

    • Getting Familiar with Data
    • What is Multi-Label Classification
    • Loading Dataset
    • Multi-Label Binarization
    • Text to TFIDF Vectors
    • Model Building and Jaccard Score
    • Improving and Saving the Model

    Sentiment Analysis using Word2Vec Text Embedding

    • What is word2vec
    • How to Get word2vec
    • Word Vectors with Spacy
    • Semantic Similarity with Spacy
    • Data Preparation
    • Data Preprocessing
    • Get word2vec from DataFrame
    • Split Dataset in Train and Test
    • ML Model Traning and Testing
    • Support Vector Machine on word2vec
    • Grid Search Cross Validation for Hyperparameters Tuning
    • Test Every Machine Learning Model

    Emotion Recognition in Text Data using GloVe Vectors Text Embedding

    • What is GloVe Vectors Part 1
    • What is GloVe Vectors Part 2
    • Download Pre-trained GloVe Vectors
    • Data Preparation
    • Preprocessing and Cleaning of Emotion Text Data
    • Load GloVe Vector
    • Text to GloVe Vectors
    • Text to GloVe on Pandas DataFrame
    • ML Model Training and Testing
    • Support Vector Machine for Emotion Recognition
    • Predict Text Emotion with Custom Data

    Resume and CV Parsing using Spacy

    • Must Read This
    • What is Resume and CV Parsing
    • How to Prepare Training Dataset
    • Data Preparation
    • NER with Spacy Part 1
    • NER with Spacy Part 2
    • NER with Spacy Part 3
    • Model Testing
    • Congrats You Have Made It!

    Sentiment Analysis using Deep Learning

    • What is Deep Learning?
    • What Makes Deep Learning State-of-the-Art?
    • How Deep Learning Works?
    • Types of Neural Networks in Deep Learning - ANN
    • Types of Neural Networks in Deep Learning - CNN
    • How Deep Learning Learns?
    • What is the Difference Between Deep Learning and Machine Learning?
    • Build ANN - Steps for Building Your First Model
    • Python Package Installation
    • Data Preprocessing
    • Get the word2vec
    • Train Test and Split
    • Feature Standardization
    • ANN Model Building and Training
    • Confusion Matrix Plot
    • Setting Custom Threshold
    • 1D CNN Model Building and Training
    • Plot Learning Curve
    • Model Load, Store and Testing

    Hate Speech Classification | Multi-Class Classification with CNN

    • Hate Speech Classification Introduction?
    • Import Python Package
    • Dataset Balancing
    • Text Preprocessing
    • Text Tokenization
    • Train Test and Split
    • Build and Train CNN
    • Model Testing
    • Testing with Custom Data
    • Load Store Model

    Poetry Generation Using Tensorflow, Keras, and LSTM

    • Introduction to Reccurent Neural Network (RNN)
    • Types of RNN
    • The Problem of RNN's or Long-Term Dependencies
    • Long Short Term Memory (LSTM) Networks
    • Sequence Generation Scheme
    • Loading Poetry Dataset
    • Tokenization
    • Prepare Training Data
    • Padding
    • LSTM Model Training
    • Poetry Generation Part 1
    • Poetry Generation Part 2

    Disaster Tweets Classification using Deep Learning Word Embeddings

    • Disaster Tweets Dataset Understanding
    • Download Dataset
    • Target Class Distribution
    • Number of Characters Distribution in Tweets
    • Number of Words, Average Words Length, and Stop words Distribution in Tweets
    • Most and Least Common Words
    • One-Shot Data Cleaning
    • Disaster Words Visualization with Word Cloud
    • Classification with TF-IDF and SVM
    • Prediction on Test Data
    • Classification with Word2Vec and SVM
    • Word Embeddings and Classification with Deep Learning Part 1
    • Word Embeddings and Classification with Deep Learning Part 2

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