Udemy

Natural Language Processing Real World Use-cases in Python

立即報名
  • 12,439 名學生
  • 更新於 8/2023
4.6
(38 個評分)
CTgoodjobs 嚴選優質課程,為職場人士提升競爭力。透過本站連結購買Udemy課程,本站將獲得推廣佣金,有助未來提供更多實用進修課程資訊給讀者。

課程資料

報名日期
全年招生
課程級別
學習模式
修業期
10 小時 23 分鐘
教學語言
英語
授課導師
Shan Singh
評分
4.6
(38 個評分)
2次瀏覽

課程簡介

Natural Language Processing Real World Use-cases in Python

Learn how to use NLTK , scikit-Learn , sentiment analysis & many more to conduct to solve Real World Problems of NLP

  • 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 solve Real 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,


課程章節

  • 5 個章節
  • 57 堂課
  • 第 1 章 Introduction
  • 第 2 章 Project 1-->> Predict the ratings of Zomato Restaurant
  • 第 3 章 Project 2-->> Predict whether news is Fake or Real
  • 第 4 章 Project 3-->> Predict the winner of the Election
  • 第 5 章 Bonus session

課程內容

  • 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


評價

  • F
    Fabrice Sanjon Tchazou
    5.0

    je suis fan

  • P
    Padiya Janki Ashokbhai
    2.5

    Exactly, Exactly, Exactly...........Lots of this and Lots of that, basically, you will see, let me just, you will clearly see that's what I heard most of the time in this course! Could've explained this better. No proper explanation of concepts I was excited to learn. When I enrolled in this course, rating was 4.7 but it's not even 4

  • M
    Mukesh Gajbhiye
    5.0

    So far so good!

  • S
    Stefano Ottaviano
    5.0

    Excelente para data science.

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