Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. NLP is about taking raw text data and deriving insights and value from it--processing text data using standard techniques in Natural Language Processing and Machine Learning. Text data is available in abundance on the Internet, whether it be reviews, tweets, surveys, web pages or emails. Natural language processing is a powerful skill that helps us derive immense value from that data. In this course, you'll first learn about using the Natural Language Toolkit to pre-process raw text. Next, you'll learn how to auto-summarize text using machine learning. You'll wrap up the course by exploring how to classify text using machine learning. By the end of this course you'll be able to confidently process raw text data and apply machine learning algorithms to it.

You will also learn some of the most advanced techniques of Data Science like Deep learning using Tensor flow package. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data. Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.

You will also learn the most advanced technique of tree based algorithm, Extreme Gradient Boosting. Sometimes, it’s debated whether XG Boost is better than Artificial Neural Network but the students are going to have both these advanced level algorithms in their arsenal.

We have also included Market Basket Analysis, and some of the popular linear and non-linear classification techniques like KNN, SVM, and Naïve Bayes. You will also get to learn one of the most popular variable reduction technique like Principal component analysis.

Introduction to Python Text Basics

Working with Text Files with Python - Part One

Working with Text Files with Python - Part Two

Introduction to Natural Language Processing

Spacy Setup and Overview

What is Natural Language Processing?

Spacy Basic

Tokenization

Stemming

Lemmatization

Stop Words

Phrase Matching and Vocabulary - Part One

Phrase Matching and Vocabulary - Part Two

Part of Speech Tagging and Entity Recognition

Introduction to POS & NER

Named Entity Recognition - Part One

Named Entity Recognition - Part Two

Sentence Segmentation

Introduction to semantics and sentiment analysis

Overview of Semantics and Word Vectors

Semantics and Word Vectors with Spacy

Sentiment Analysis Overview

Sentiment Analysis with NLTK

Sentiment Analysis Code Along Movie Review Project

Sentiment Analysis Code - Capstone Project

Topic Modeling

Introduction to Topic Modeling Section

Latent Dirichlet Allocation Overview

Latent Dirichlet Allocation with Python - Part One

Latent Dirichlet Allocation with Python - Part Two

Non-negative Matrix Factorization with Python

Topic Modeling Project - Capstone Project

Introduction to KNN

Theory of KNN

Using KNN to classify in Python

Validation of the model

Project work with KNN

Introduction to SVM

Theory of SVM

Using SVM to classify in Python

Validation of the model

Kernel trick of SVM to increase accuracy

Learn about different kernel

Project work with KNN

Introduction to Naïve Bayes

Theory of Naïve Bayes

Using Naïve Bayes to classify in Python

Validation of the model

Project work with Naïve Bayes

Introduction to MBA

Theory of MBA

Lift, support & confidence

Create recommendation with MBA

Project work with MBA

Introduction to PCA

Theory of PCA

How many components are considered?

Loading of components

Project work with PCA

Keras and Tensorflow

The neuron

The activation function

How do neural network learn

Gradient Descent

Backpropagation

ANN for regression

ANN for classification

Project work on ANN regression

Project work on ANN classification

XGBoost intuition

Create XGBoost matrices

Setting up parameters

Predicting with XGBoost

Validation of the model

Tuning of parameters

Cross validation

Driver importance

Project work on XGBoost