Nlp And Python Development: Basics To Advanced Applications

Unlock the power of NLP and Python to create intelligent applications and advanced machine learning models

What you'll 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.

Requirements

Basic Knowledge of Python: Understanding of basic programming concepts and experience with Python is necessary.

Interest in NLP and Machine Learning: A keen interest in Natural Language Processing and Machine Learning will be beneficial.

Basic Understanding of Programming Concepts: Familiarity with variables, loops, and functions.

Access to a Computer: A computer with internet access for downloading necessary tools and libraries.

Python Environment Setup: Basic knowledge of setting up a Python environment using tools like Anaconda.

Description

Section 1: IntroductionIn 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 ChatbotIn 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 CalculatorThis 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.ConclusionThroughout 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.

Overview

Section 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

Section 2: Python Case Study - Create Chatbot

Lecture 13 Introduction to Project

Lecture 14 Downloading Understating

Lecture 15 Installation of Tools Anaconda and NLTK

Lecture 16 Reflection Dictionary

Lecture 17 Pairs

Lecture 18 Checking Output Part 1

Lecture 19 Checking Output Part 2

Lecture 20 Checking Output Part 3

Lecture 21 Checking Output Part 4

Section 3: Python GUI Case Study - Creating a Calculator

Lecture 22 Introduction of Project

Lecture 23 How to Develop Calculation Application

Lecture 24 IDE Explanation

Lecture 25 Importing Libraries

Lecture 26 Tkinter

Lecture 27 Code Gui Buttons

Lecture 28 Widgets of Tkinter

Lecture 29 Logic Behind Calculator

Lecture 30 Function Call of Calculator

Lecture 31 Simple Calculator Implementation Output

Lecture 32 Code Scientific Calculator

Lecture 33 Code Calculator Part 1

Lecture 34 Code Calculator Part 2

Lecture 35 Code Calculator Part 3

Lecture 36 Final and Spyder Output

Lecture 37 Introduction to Machine Learning

Lecture 38 Advantages and Disadvantages of Machine Learning

Lecture 39 NumPy Introduction

Lecture 40 Features and Installation

Lecture 41 NumPy Array Creation

Lecture 42 NumPy Array Attributes

Lecture 43 NumPy Array Operations

Lecture 44 NumPy Array Operations Continue

Lecture 45 NumPy Array Unary Operations

Lecture 46 Numpy Array Splicing

Lecture 47 NumPy Array Shpe

Lecture 48 Stacking Together Different Arrays

Lecture 49 Splitting one Array into Several Smaller ones

Lecture 50 Copies and Views

Lecture 51 NumPy Array Indexing

Lecture 52 NumPy Array Indexing Continue

Lecture 53 NumPy Array Boolean

Lecture 54 Introduction to Matlplotlib

Lecture 55 Understanding Various Functions of Pyplot

Lecture 56 Multiple Figures and Subplots

Lecture 57 Intro to Pandas

Lecture 58 Intro to Pandas Continue

Lecture 59 Data Structure in Pandas

Lecture 60 Data Structure in Pandas Continue

Lecture 61 Pandas Column Select

Lecture 62 Remove Operations

Lecture 63 Pandas Arithmetic Operations

Lecture 64 Pandas Arithmetic Operations Continue

Lecture 65 Introduction to Scikit Learn

Lecture 66 Supervised

Lecture 67 Unsupervised Learning

Lecture 68 Load Data Set

Lecture 69 Scikit Example Digits

Lecture 70 Digits Dataset Using Matplotlib

Lecture 71 Understading Metrics of Predicted Digits Dataset

Lecture 72 Persisting Models

Lecture 73 K-NN Algorithm with Example

Lecture 74 Cross Validation

Lecture 75 Cross Validation Techniques

Lecture 76 K-Means Clustering Example

Lecture 77 Agglomeration

Lecture 78 PCA Pipeline

Lecture 79 Face Recognition

Lecture 80 Face Recognition Output

Lecture 81 Right Estimator

Lecture 82 Text Data Example

Lecture 83 Extracting Features

Lecture 84 Occurrences to Frequencies

Lecture 85 Classifier Training

Lecture 86 Performance Analysis on the Test Set

Lecture 87 Parameter Tuning

Lecture 88 Language Identifcation

Lecture 89 Movie Review Screen Stream

Lecture 90 Movie Review Screen Stream Continue

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