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NLP and Python Development: Basics to Advanced Applications

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NLP and Python Development: Basics to Advanced Applications, available at $44.99, has an average rating of 4.5, with 90 lectures, based on 1 reviews, and has 67 subscribers.

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You will learn about The fundamentals of Natural Language Processing (NLP) and its applications. Text preprocessing techniques such as tokenization, stemming, lemmatization, and removing stopwords. Feature extraction methods to convert text into numerical data. How to install and set up essential NLP libraries and tools. Practical implementation of NLP concepts through hands-on demos. Creating a chatbot using Python, including reflection dictionaries and output verification. Developing a GUI calculator application with Python's Tkinter library. An introduction to machine learning, its advantages and disadvantages. Utilizing NumPy for array creation, operations, and manipulation. Exploring data visualization with Matplotlib and data handling with Pandas. Supervised and unsupervised learning techniques using Scikit-Learn. Real-world applications such as face recognition, text classification, and sentiment analysis. This course is ideal for individuals who are Aspiring Data Scientists: Individuals aiming to build a career in data science and machine learning. or Python Programmers: Python developers looking to expand their skills into NLP and machine learning. or Data Analysts: Professionals seeking to enhance their data analysis skills with advanced techniques. or Students: Computer science and engineering students interested in learning about NLP and machine learning. or AI Enthusiasts: Anyone with a passion for artificial intelligence and natural language processing. or Software Developers: Developers wanting to integrate NLP capabilities into their applications. or Researchers: Academics and researchers needing practical knowledge of NLP and machine learning for their work. or Tech Entrepreneurs: Entrepreneurs looking to implement machine learning solutions in their startups. or IT Professionals: IT professionals seeking to upskill and transition into data science roles. or Self-Learners: Individuals motivated to learn about cutting-edge technologies in NLP and machine learning on their own. It is particularly useful for Aspiring Data Scientists: Individuals aiming to build a career in data science and machine learning. or Python Programmers: Python developers looking to expand their skills into NLP and machine learning. or Data Analysts: Professionals seeking to enhance their data analysis skills with advanced techniques. or Students: Computer science and engineering students interested in learning about NLP and machine learning. or AI Enthusiasts: Anyone with a passion for artificial intelligence and natural language processing. or Software Developers: Developers wanting to integrate NLP capabilities into their applications. or Researchers: Academics and researchers needing practical knowledge of NLP and machine learning for their work. or Tech Entrepreneurs: Entrepreneurs looking to implement machine learning solutions in their startups. or IT Professionals: IT professionals seeking to upskill and transition into data science roles. or Self-Learners: Individuals motivated to learn about cutting-edge technologies in NLP and machine learning on their own.

Enroll now: NLP and Python Development: Basics to Advanced Applications

Summary

Title: NLP and Python Development: Basics to Advanced Applications

Price: $44.99

Average Rating: 4.5

Number of Lectures: 90

Number of Published Lectures: 90

Number of Curriculum Items: 90

Number of Published Curriculum Objects: 90

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

  • The fundamentals of Natural Language Processing (NLP) and its applications.
  • Text preprocessing techniques such as tokenization, stemming, lemmatization, and removing stopwords.
  • Feature extraction methods to convert text into numerical data.
  • How to install and set up essential NLP libraries and tools.
  • Practical implementation of NLP concepts through hands-on demos.
  • Creating a chatbot using Python, including reflection dictionaries and output verification.
  • Developing a GUI calculator application with Python's Tkinter library.
  • An introduction to machine learning, its advantages and disadvantages.
  • Utilizing NumPy for array creation, operations, and manipulation.
  • Exploring data visualization with Matplotlib and data handling with Pandas.
  • Supervised and unsupervised learning techniques using Scikit-Learn.
  • Real-world applications such as face recognition, text classification, and sentiment analysis.

Who Should Attend

  • Aspiring Data Scientists: Individuals aiming to build a career in data science and machine learning.
  • Python Programmers: Python developers looking to expand their skills into NLP and machine learning.
  • Data Analysts: Professionals seeking to enhance their data analysis skills with advanced techniques.
  • Students: Computer science and engineering students interested in learning about NLP and machine learning.
  • AI Enthusiasts: Anyone with a passion for artificial intelligence and natural language processing.
  • Software Developers: Developers wanting to integrate NLP capabilities into their applications.
  • Researchers: Academics and researchers needing practical knowledge of NLP and machine learning for their work.
  • Tech Entrepreneurs: Entrepreneurs looking to implement machine learning solutions in their startups.
  • IT Professionals: IT professionals seeking to upskill and transition into data science roles.
  • Self-Learners: Individuals motivated to learn about cutting-edge technologies in NLP and machine learning on their own.

Target Audiences

  • Aspiring Data Scientists: Individuals aiming to build a career in data science and machine learning.
  • Python Programmers: Python developers looking to expand their skills into NLP and machine learning.
  • Data Analysts: Professionals seeking to enhance their data analysis skills with advanced techniques.
  • Students: Computer science and engineering students interested in learning about NLP and machine learning.
  • AI Enthusiasts: Anyone with a passion for artificial intelligence and natural language processing.
  • Software Developers: Developers wanting to integrate NLP capabilities into their applications.
  • Researchers: Academics and researchers needing practical knowledge of NLP and machine learning for their work.
  • Tech Entrepreneurs: Entrepreneurs looking to implement machine learning solutions in their startups.
  • IT Professionals: IT professionals seeking to upskill and transition into data science roles.
  • Self-Learners: Individuals motivated to learn about cutting-edge technologies in NLP and machine learning on their own.

Section 1: Introduction

In this section, students will delve into the foundational concepts of Natural Language Processing (NLP). The journey begins with an introduction to NLP, setting the stage for understanding how machines can interpret and respond to human language. Students will learn about text preprocessing, including techniques such as replacing contractions, tokenization, and removing stop words, which are essential for preparing text data for analysis. Feature extraction will be covered to help students understand how to transform text into numerical representations suitable for machine learning algorithms. The section concludes with hands-on sessions demonstrating the installation of NLP tools and libraries, followed by a practical demo to reinforce the concepts learned.

Section 2: Python Case Study – Create Chatbot

In this case study, students will apply their NLP knowledge to create a chatbot using Python. The project kicks off with an introduction and understanding of the necessary tools, including Anaconda and NLTK. Students will learn to create reflection dictionaries and pairs, essential components for chatbot responses. The section involves multiple stages of checking and refining the output, ensuring students can develop a functional and interactive chatbot. This hands-on project will solidify their understanding of how NLP can be applied in real-world applications.

Section 3: Python GUI Case Study – Creating a Calculator

This section transitions into graphical user interface (GUI) development using Python. Students will embark on a project to create a calculator application, starting with an introduction and a detailed explanation of the integrated development environment (IDE). They will learn to import necessary libraries, use Tkinter for GUI development, and code various elements such as buttons and widgets. The section covers the logic behind the calculator, function calls, and implementation of both simple and scientific calculators. By the end of this section, students will have a comprehensive understanding of Python GUI development and its applications.

Conclusion

Throughout this course, students will gain extensive knowledge and practical experience in Natural Language Processing, chatbot creation, and Python GUI development. By working on real-world projects, they will not only learn theoretical concepts but also apply them in practical scenarios, enhancing their problem-solving skills and technical proficiency. This comprehensive course is designed to equip students with the necessary tools and techniques to excel in the field of machine learning and application development.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Intoroduction to NLP

Lecture 2: Text Preprocessing

Lecture 3: Feature Extraction

Lecture 4: NLP Installation

Lecture 5: NLP – Demo

Lecture 6: Replacing Contractions

Lecture 7: Tokenize Dataset

Lecture 8: Remove Stopwords

Lecture 9: Stemming and Lemmatization

Lecture 10: Stemming and Lemmatization Continues

Lecture 11: Convert Token No Stopwords

Lecture 12: Machine Learning Algorithms

Chapter 2: Python Case Study – Create Chatbot

Lecture 1: Introduction to Project

Lecture 2: Downloading Understating

Lecture 3: Installation of Tools Anaconda and NLTK

Lecture 4: Reflection Dictionary

Lecture 5: Pairs

Lecture 6: Checking Output Part 1

Lecture 7: Checking Output Part 2

Lecture 8: Checking Output Part 3

Lecture 9: Checking Output Part 4

Chapter 3: Python GUI Case Study – Creating a Calculator

Lecture 1: Introduction of Project

Lecture 2: How to Develop Calculation Application

Lecture 3: IDE Explanation

Lecture 4: Importing Libraries

Lecture 5: Tkinter

Lecture 6: Code Gui Buttons

Lecture 7: Widgets of Tkinter

Lecture 8: Logic Behind Calculator

Lecture 9: Function Call of Calculator

Lecture 10: Simple Calculator Implementation Output

Lecture 11: Code Scientific Calculator

Lecture 12: Code Calculator Part 1

Lecture 13: Code Calculator Part 2

Lecture 14: Code Calculator Part 3

Lecture 15: Final and Spyder Output

Lecture 16: Introduction to Machine Learning

Lecture 17: Advantages and Disadvantages of Machine Learning

Lecture 18: NumPy Introduction

Lecture 19: Features and Installation

Lecture 20: NumPy Array Creation

Lecture 21: NumPy Array Attributes

Lecture 22: NumPy Array Operations

Lecture 23: NumPy Array Operations Continue

Lecture 24: NumPy Array Unary Operations

Lecture 25: Numpy Array Splicing

Lecture 26: NumPy Array Shpe

Lecture 27: Stacking Together Different Arrays

Lecture 28: Splitting one Array into Several Smaller ones

Lecture 29: Copies and Views

Lecture 30: NumPy Array Indexing

Lecture 31: NumPy Array Indexing Continue

Lecture 32: NumPy Array Boolean

Lecture 33: Introduction to Matlplotlib

Lecture 34: Understanding Various Functions of Pyplot

Lecture 35: Multiple Figures and Subplots

Lecture 36: Intro to Pandas

Lecture 37: Intro to Pandas Continue

Lecture 38: Data Structure in Pandas

Lecture 39: Data Structure in Pandas Continue

Lecture 40: Pandas Column Select

Lecture 41: Remove Operations

Lecture 42: Pandas Arithmetic Operations

Lecture 43: Pandas Arithmetic Operations Continue

Lecture 44: Introduction to Scikit Learn

Lecture 45: Supervised

Lecture 46: Unsupervised Learning

Lecture 47: Load Data Set

Lecture 48: Scikit Example Digits

Lecture 49: Digits Dataset Using Matplotlib

Lecture 50: Understading Metrics of Predicted Digits Dataset

Lecture 51: Persisting Models

Lecture 52: K-NN Algorithm with Example

Lecture 53: Cross Validation

Lecture 54: Cross Validation Techniques

Lecture 55: K-Means Clustering Example

Lecture 56: Agglomeration

Lecture 57: PCA Pipeline

Lecture 58: Face Recognition

Lecture 59: Face Recognition Output

Lecture 60: Right Estimator

Lecture 61: Text Data Example

Lecture 62: Extracting Features

Lecture 63: Occurrences to Frequencies

Lecture 64: Classifier Training

Lecture 65: Performance Analysis on the Test Set

Lecture 66: Parameter Tuning

Lecture 67: Language Identifcation

Lecture 68: Movie Review Screen Stream

Lecture 69: Movie Review Screen Stream Continue

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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