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2021-02-17 08:01:33
30-Day Money-Back Guarantee
IT & Software Other IT & Software Artificial Intelligence

Natural Language Processing Real-World Projects in Python

Solve 3 real Business Problems. Build Robust AI, NLP models for Sentiment, Security & Stock News Domain..
Hot & New
Rating: 4.4 out of 54.4 (36 ratings)
18,387 students
Created by Shan Singh
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Hands on Real-World Projects on Various Domains of Natural Language Processing
  • Develop Natural Language Processing Models to Customer Sentiments
  • Develop Natural Language Processing Models to predict Stock News
  • Develop Natural Language Processing Models to Predict the Strength of password

Requirements

  • Basic knowledge of programming is recommended. However, You can follow my Basics of Python Course which is free of cost therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science and directly apply these skills to solve real world challenging business problems.

Description

Are you looking to land a top-paying job in Data Science , AI & 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  Data Science and Artificial Intelligence?


If the answer is yes to any of these questions, then this course is for you!

Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology.

In business, Data Science is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.


1.Task #1 @Predict Customer Sentiments  : Develop an AI model to predict Customer Sentiments of Amazon..

3.Task #2 @Predict future Stock Prices: Develop NLP models to predict future Stock prices.

2.Task #3 @Predict the strength of a Password: Predict the category of Password whether it is Strong, Good or Weak.

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/NLP Engineer

  • It shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation.


  • 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

  • Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.


Who this course is for:

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

Course content

4 sections • 36 lectures • 5h 17m total length

  • Preview03:01
  • Preview02:15

  • Preview01:27
  • Datasets & Resources
    00:01
  • Preview08:06
  • Apply Exploratory Data Analysis on Data
    06:30
  • Intuition behind Bag of Words
    15:48
  • Intuition behind Logistic Regression --part 1
    13:52
  • Intuition behind Logistic Regression --part 2
    14:42
  • Apply Bag of Words on data
    13:38
  • Automate your NLP model & Machine Learning Model
    13:23
  • Preview08:30
  • Intuition behind TF-IDF --part 2
    08:15
  • Applying algorithms of NLP & Machine Learning
    06:47
  • Data Preparation for Modelling Purpose
    06:57
  • What is Imbalance Data & how to handle it??
    19:41
  • Part1-- What is Cross-validation & when to use it??
    10:45
  • Part2-- What is Cross-validation & when to use it??
    13:01
  • Applying Techniques of Handling Imbalance Data & Cross Validation
    14:07

  • Introduction to Business Problem & Dataset
    01:24
  • Datasets & Resources
    00:01
  • Data Pre-processing on Data.
    09:15
  • Perfrom Data Wrangling & Merging
    06:45
  • Preview20:39
  • Intuition behind Random Forest --part 2
    11:38
  • Apply Bag of words and Random forest on Data
    07:00
  • Model Evaluation..
    06:54
  • Intuition Behind Naive Bayes-Part 1
    17:52
  • Intuition Behind Naive Bayes- Part 2
    20:46
  • Apply Naive Bayes on Data..
    05:05

  • Introduction to Business Problem & Dataset
    01:14
  • Datasets & Resources
    00:01
  • Preview08:25
  • Apply TF-IDF on data
    08:42
  • Apply Logistic Regression on Data
    07:06
  • Checking Accuracy of Model
    03:52

Instructor

Shan Singh
Data Scientist
Shan Singh
  • 4.3 Instructor Rating
  • 792 Reviews
  • 91,480 Students
  • 9 Courses

Professionally, I am a Data Scientist having experience of 6 years in finance, retail and transport.From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you!

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