Spacy for NLP

Created by Selva Prabhakaran

60 Video Lessons
English
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4h 29m of Self-paced videos
15-Day Moneyback Guarantee
Learning duration ~ 2-3 weeks
Complete access for 1 year

What you will learn

Setup
Spacy

Tokenization in
NLP

POS
Tagging

Rule Based
Matching

Word 2
Vector

Training the
NLP models

Chatbots with Spacy
and Rasa

Course Curriculum

Requirements

Basics of Python
Foundational knowledge of Data Science
High school maths

Who should attend this course?

Data Science Aspirants
Data Science Professionals
Software/Data engineers interested in quantitative analysis
Professionals working with large datasets
Data analysts, economists, researchers

About the course

You will learn the following skills by the end of the course:

LightGBM
XGBoost Random
Forest Decision Tree
Logistic Regression
Hyperparameter
Tuning Feature Importance Confusion Matrix
ROC AUC
Concordance and Discordance
Precision Recall Curve
Capture Rates and Gains
Feature Engineering
Label Encoding
Frequency Encoding
Chi-Square test ANOVA test
Exploratory Data Analysis
Memory
Optimization
Data Preprocessing

Instructor

Selva Prabhakaran Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this project!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

4.5+Instructor rating
200+ reviews
10K+students
15+ Courses

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Spacy for NLP

What you will learn

Setup Spacy

Tokenization in NLP

POS Tagging

Rule Based Matching

Word 2 Vector

Training the NLP models

Chatbots with Spacy and Rasa