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Machine Learning For Beginners - Sentiment Analyzer
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.99 GB | Duration: 3h 25m
Sentiment Analyzer Project - TF & IDF
What you'll learn
Analyze and Interpret Sentiment: Extract and quantify emotions, sentiments, and opinions from text data using various sentiment analysis techniques.
2. Master Sentiment Analysis Tools: Learn to work with popular libraries (NLTK, spaCy, TensorFlow) and tools (TextBlob, VaderSentiment) for sentiment analysis.
Develop NLP Skills: Understand Natural Language Processing (NLP) fundamentals, text preprocessing, and machine learning approaches for sentiment classification.
Apply Sentiment Analysis in Real-World Scenarios: Confidently apply sentiment analysis techniques to real-world applications, such as customer feedback analysis
Requirements
Python programming knowledge is needed to pursue this course
Description
Sentiment Analysis: Extracting Insights from TextUnlock the power of emotions in text data with Sentiment Analysis. This comprehensive course teaches you to extract, analyze, and quantify sentiments, opinions, and emotions from various text sources.Key Topics:- Fundamentals of Natural Language Processing (NLP)- Sentiment Analysis techniques (rule-based, machine learning, deep learning)- Text preprocessing and feature extraction- Sentiment classification and visualization- Handling sarcasm, irony, and figurative language- Real-world applications (social media, customer feedback, product reviews)Learning Outcomes:- Analyze and interpret sentiments from text data- Master sentiment analysis tools and libraries (NLTK, spaCy, TensorFlow)- Develop NLP skills for text preprocessing and machine learning- Apply sentiment analysis in real-world scenariosTarget Audience:- Data scientists and analysts- NLP enthusiasts- Marketing and customer service professionals- Researchers and academicsChallenges:1. Handling sarcasm, irony, and figurative language2. Dealing with noisy or incomplete data3. Maintaining accuracy across domains4. Handling multilingual text data5. Integrating with existing systemsBy working on a Sentiment Analysis project, you'll gain hands-on experience with NLP, machine learning, and data analysis, while extracting valuable insights from text data.Prerequisites: Basic Python programming skillsJoin this course to unlock valuable insights from text data and drive informed decisions.Thank You and Keep Learning!!
Overview
Section 1: Introduction
Lecture 1 Introduction - What this course is all about
Section 2: Sentriment Analyzer
Lecture 2 Understand the Project and code for sentiment analyzer
Lecture 3 Understand the use of libraries - python
Section 3: Understand the thing behind the scene
Lecture 4 Understand the term frequency
Lecture 5 Understand the DF and IDF
Lecture 6 How TF and IDF works under the hood
Lecture 7 How CountVectorizer works
Lecture 8 Baye's Theorem and It's use in real life
Lecture 9 Use Baye's theorem to identify spam mails
Lecture 10 Baye's theorem and sentiment analysis
Lecture 11 Significance of Training and Test data
Lecture 12 fit_transform and transform methods
Lecture 13 Save your model and use it in client code
Lecture 14 Model with multiple features
Lecture 15 Accuracy report
Lecture 16 Run your project having multiple features
Lecture 17 Important Docs and Artefacts
This course is for the beginners in machine learning who want to learn basics without having prior knowledge of Maths
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