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Natural Language Processing Real World Use-cases in Python

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Natural Language Processing Real World Use-cases in Python, available at $49.99, has an average rating of 4.35, with 61 lectures, based on 38 reviews, and has 12418 subscribers.

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You will learn about Hands on Real-World Projects on Various Domains of Natural Language Processing Build Natural Language Processing & ML Models to solve a real world problem Develop Natural Language Processing Models to predict Whether news is fake or real How to perform Setiment analysis This course is ideal for individuals who are One who is curious about to do Carrier Transition into Data Science, Machine learning & Natural Language Processing, It is particularly useful for One who is curious about to do Carrier Transition into Data Science, Machine learning & Natural Language Processing,.

Enroll now: Natural Language Processing Real World Use-cases in Python

Summary

Title: Natural Language Processing Real World Use-cases in Python

Price: $49.99

Average Rating: 4.35

Number of Lectures: 61

Number of Published Lectures: 57

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 57

Original Price: ₹1,199

Quality Status: approved

Status: Live

What You Will Learn

  • Hands on Real-World Projects on Various Domains of Natural Language Processing
  • Build Natural Language Processing & ML Models to solve a real world problem
  • Develop Natural Language Processing Models to predict Whether news is fake or real
  • How to perform Setiment analysis

Who Should Attend

  • One who is curious about to do Carrier Transition into Data Science, Machine learning & Natural Language Processing,

Target Audiences

  • One who is curious about to do Carrier Transition into Data Science, Machine learning & Natural Language Processing,

  • Are you looking to land a top-paying job in Data Science/Natural Language Processing?

  • Or are you a seasoned AI practitioner who want to take your career to the next level?

  • Or are you an aspiring data scientist who wants to get Hands-on  Natural Language Processing and Machine Lrarning?

Welcome to the course of Real world use-cases on Natural Language Processing ! This course is specifically designed to be ready for Job perspective in Natural Language Processing domain using Python programming language.

In the course we will cover everything you need to learn in order to solveReal World Challenges in NLP with Python.

We’ll start off with the basics, learning how to open and work with text and csv files with Python, as well as we will learn how to clean & manipulate data & how to use regular expressions to search for custom patterns inside of text data…

Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as we will cover intuition behind tokenization, Stemming and lemmatization of text.

1.Project #1 @Predict Ratings of a Zomato Resaturant : Develop an AI/NLP model to predict Ratings of Zomato Restaurants..

2.Project #2 @Predict Whether News is Fake or Real: Predict whether news is fake or Real by building Pipeline..

3.Project #3 @Predict Winner of a Election : Perform Sentiment Analysis to Predict winner of a election..

Why should you take this Course?

  • It explains Projects on  real Data and real-world Problems. No toy data! This is the simplest & best way to become a  Data Scientist/AI Engineer/ ML Engineer

  • It shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging ,wrangling and concatenating data , perform advance Exploratory Data Analysis &  preparing and processing data for Statistics, Machine Learning , NLP & to come up with meaningful insights at the end..

  • In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion

  • Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums which is available for you 24*7

  • Guaranteed Satisfaction : All of this comes with a 30 day money back guarantee, so you can try the course risk free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course & course benefits

Lecture 2: Utilize QnA Section ( Golden Opportunity ) !

Lecture 3: how to follow this course-must watch

Lecture 4: Introduction to Jupyter Notebook

Chapter 2: Project 1–>> Predict the ratings of Zomato Restaurant

Lecture 1: Datasets & Resources

Lecture 2: Understanding your Data & finding missing values inside it

Lecture 3: Pre-process Data & Remove duplicate values

Lecture 4: Perform Discretisation to ready your Target Feature..

Lecture 5: Prepare your reviews list feature using regular expression

Lecture 6: How to Generate your word-cloud

Lecture 7: Data Cleaning on reviews list

Lecture 8: What are Stopwords & how to remove it from our data

Lecture 9: What is Unigram & how to Perform Unigram Analysis

Lecture 10: What are Bigram & Trigrams and how to perform Bigram & TriGram Analysis…

Lecture 11: Analysing Most famous Cuisines

Lecture 12: How to prepare our data for Machine Learning..

Lecture 13: how to Lemmatize your data..

Lecture 14: Perform Feature Encoding on data

Lecture 15: Intuition behind TF-IDF part-1

Lecture 16: Intuition behind TF-IDF part-2

Lecture 17: Applying TF-IDF on your Text data

Lecture 18: Intuition Behind Logistic Regression – Part 1

Lecture 19: Intuition Behind Logistic Regression – Part 2

Lecture 20: Build Logistic Regression model & evaluate it..

Lecture 21: Intuition Behind Naive Bayes – Part 1

Lecture 22: Intuition Behind Naive Bayes – Part 2

Lecture 23: Intuition behind Decision Tree – Part 1

Lecture 24: Intuition behind Decision Tree – Part 2

Lecture 25: Intuition behind Decision Tree – Part 3

Lecture 26: Intuition behind Decision Tree – Part 4

Lecture 27: Intuition behind Decision Tree – Part 5

Lecture 28: Intuition behind Decision Tree – Part 6

Lecture 29: Intuition Behind Random Forest- Part 1

Lecture 30: Intuition Behind Random Forest- Part 2

Lecture 31: Intuition Behind Linear Regression- Part 1

Lecture 32: Intuition Behind Linear Regression- Part 2

Lecture 33: Intuition Behind Linear Regression- Part 3

Lecture 34: Intuition Behind KNN- Part 1

Lecture 35: Intuition Behind KNN- Part 2

Lecture 36: Intuition Behind KNN- Part 3

Lecture 37: Intuition Behind KNN- Part 4

Lecture 38: Playing with Multiple classification Algorithms..

Lecture 39: Part1– What is Cross-validation & when to use it??

Lecture 40: Part2 — What is Cross-validation & when to use it??

Lecture 41: How to Cross validate your Machine learning model..

Chapter 3: Project 2–>> Predict whether news is Fake or Real

Lecture 1: Datasets & Resources

Lecture 2: How to obtain label distribution

Lecture 3: Intuition Behind Bag of words

Lecture 4: Applying TF_IDF & Bag-of-words on Data

Lecture 5: How to build a Pipeline..

Lecture 6: Applying Multiple Algorithms on Data

Chapter 4: Project 3–>> Predict the winner of the Election

Lecture 1: Datasets & Resources

Lecture 2: How to Perform Sentiment Analysis on data

Lecture 3: How to Pre-Process our data..

Lecture 4: Lets Perform Data Cleaning on data..

Lecture 5: Perform Data Analysis on Data.

Chapter 5: Bonus session

Lecture 1: Bonus Section

Instructors

  • Shan Singh
    Shan Singh
    Top Rated & Best-Selling Udemy Instructor , Data Scientist

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 4 votes
  • 4 stars: 13 votes
  • 5 stars: 19 votes

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