Natural Language Processing (NLP) with Python

Natural Language Processing with Python

Target audience
Anyone who want to perform natural language analysis with Python but have not received extensive training on this subject before
Scope
Usage of Miniconda, Visual Studio, Git, GitHub repository,
Coding with Python - pandas, snawballstemmer, scikit-learn, gensim, wordcloud libraries,
Natural language processing through Turkish language; It includes data cleaning, digitization, conversion to vectors, identifying similarities, determining the mood of texts taken from social media with machine learning, and visualizing these data.
Method
In the Video content, all written codes are explained step by step, through the assignment what has been learnt is transferred into the code. The project gives the participant the opportunity to use what he has learned in a new concept.

Training Content

1. Basic requirements
2. Git installation
3. Miniconda installation
4. Visual Studio installation, Git and Miniconda definitions
5. Git bash
6. Bonus: debugging
7. JupyterLab installation
8. Using GitHub
1. Curriculum
2. Reading data
1. Data cleaning
2. Preparing data cleaning steps in Python file and Git push
1. BoW method
2. TF-IDF method
3. Word2vec method
4. Preparing the methods in Python file and Git push
1. Introduction to machine learning
2. SVM algorithm
3. Using SVM algorithm with word2vec
4. KNN algorithm
5. Git push
1. Word2vec vector properties
2. Visualization of word2vec vectors and Git push
1. Summary
2. Assignment

Video Training
Online Training
On-site Training

Natural Language Processing with Python

Training Content

Application Installations

Introduction

Data Cleaning

Digitization (Representation) of Data

Machine Learning

Visualization of Textual Data

Wrap-up

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