Natural Language Processing Real-World Projects in Python

Solve 3 real Business Problems. Build Robust AI, NLP models for Sentiment, Security & Stock News Domain..

4.37 (28 reviews)

Natural Language Processing Real-World Projects in Python



5.5 hours


Jan 2021

Last Update
Regular Price

What you will 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


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.


Introduction to this course

Intro To course

How to follow this course-Must Watch

Project 1-->>Predict the sentiments of Amazon Customer

Datasets & Resources

Introduction to Business Problem & Dataset

Perform Data Pre-processing on Amazon Data

Apply Exploratory Data Analysis on Data

Intuition behind Bag of Words

Intuition behind Logistic Regression --part 1

Intuition behind Logistic Regression --part 2

Apply Bag of Words on data

Automate your NLP model & Machine Learning Model

Intuition behind TF-IDF --part 1

Intuition behind TF-IDF --part 2

Applying algorithms of NLP & Machine Learning

Data Preparation for Modelling Purpose

Applying Techniques of Handling Imbalance Data & Cross Validation

Project2---->>> Predict the Stock News Headlines

Datasets & Resources

Introduction to Business Problem & Dataset

Data Pre-processing on Data.

Perfrom Data Wrangling & Merging

Intuition Behind Random Forest Part-1

Intuition behind Random Forest --part 2

Apply Bag of words and Random forest on Data

Model Evaluation..

Intuition Behind Naive Bayes-Part 1

Intuition Behind Naive Bayes- Part 2

Apply Naive Bayes on Data..

Project 3-->>> Predicting the strength of Password

Datasets & Resources

Introduction to Business Problem & Dataset

Exploring Data

Apply TF-IDF on data

Apply Logistic Regression on Data

Checking Accuracy of Model


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