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

[b]What you'll learn[/b]

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

[b]Requirements[/b]

Python programming knowledge is needed to pursue this course

[b]Description[/b]

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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